Methods to formulate treatments for individual cancer patients by assessing genomic and/or phenotypic differences between cancer and normal tissues and integrating the results to identify dysfunctional pathways are described.
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1. A method to identify at least one therapeutic target in an individual cancer patient, which method consists essentially of:
(a) assaying multiple characteristics of the genome and/or multiple characteristics of the molecular phenotype in a biopsy of the cancer afflicting said patient to obtain one or more first data sets wherein each data set consists of a single type of characteristic and assaying said characteristics in normal tissue from said individual patient to obtain one or more second data sets, wherein said multiple characteristics represent a sufficient fraction of the networks of interacting genes and their corresponding proteins in any said patient to permit identification of at least one dysregulated pathway;
(b) identifying characteristics from (a) in said one or more first data sets which differ from those in said one or more second data sets to obtain differentiated characteristics; and
(c) matching said differentiated characteristics of (b) to pathways to identify one or more pathways dysregulated in said cancer biopsy as compared to normal tissue for therapeutic intervention wherein
(i) each pathway comprises a multiplicity of interacting proteins curated from the literature;
(ii) the differentiated characteristics are used to identify dysregulated pathways by applying statistical approaches based on triangulation to each pathway as a whole; and
(iii) whereby one or more pathways is determined to be dysregulated in said individual patient's cancer as compared to the patient's normal tissue; and
(d) identifying at least one therapeutic target wherein interaction with said target would overcome dysregulation of said pathway;
wherein step (b) and/or the step (c) is performed by a computer; and
wherein each said identified dysregulated pathway contains a sufficient number of independent data points to overcome statistical limitations of one or two samples and exhibits a coherent pattern whereby gene products are dysregulated in accordance with the pathway itself; and
said target is responsive to approved or investigational drugs or biologics.
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This application claims benefit of U.S. Ser. No. 61/115,898 filed 18 Nov. 2008, the contents of which are incorporated herein by reference.
The invention relates to analysis of tumor tissue of individual patients to determine irregularities in gene expression that leads to identifying suitable treatments. Because each patient is individually analyzed for expression abnormalities, tailoring treatment protocols to the particular malignant tissue present in the patient is possible.
There is a plethora of known drugs sometimes used singly, but mostly in combination, for treating solid and blood tumors in humans. Chemotherapeutic approaches using small molecules such a vincristine, gemcitabine, 5-fluorouracil, and a litany of others (such as Gleevec®) which are used mostly in combination therapies, is widespread. In addition, biologicals such as Rituxan®, Erbitux™, and Herceptin® have also been used. With the exception of Herceptin®, and Gleevec® which targets an abnormal BCR-ABL protein found in patients with chronic myelogenous leukemia and a few others, these treatments are generally applied to individual patients based on guesswork rather than analysis. The heterogeneity of tumor types, even within a given organ such as breast or prostate, makes it difficult to ascertain in advance whether an individual patient's malignancy will, or will not, be responsive to any particular protocol. To applicants' knowledge, at least until recently, only the administration of Herceptin® was systematically based on the results of a companion diagnostic on an individual patient for an indication of whether (or not) the tumor will respond. More recently, other attempts to individualize treatment have been implemented, including chemosensitivity screening and tests for an individual target (e.g., KRAS mutations) which are used by some doctors. Estrogen receptor screening is often done routinely before administration of tamoxifen.
Studies have been done, however, with respect to tumor types in pools of many patient samples derived from a given organ or of cancers of a particular type to identify, in general, which metabolic or signal transduction/biological pathways are dysregulated in tumors of various types and which genes are over-expressed- or under-expressed. For example, studies of micro-RNA production patterns in ovarian cancer have been conducted by Dahiya, N., et al., PLoS ONE (2008) 18:e2436. Attempting to find such patterns on an individual basis has been limited to the recently reported sequencing of the entire genome of tumor cells from an individual patient at the cost of over $1 million, and as the patient had died before the project began, it too was not aimed at treatment of the patient herself. As the costs of sequencing have come down dramatically, a number of groups are conducting studies which attempt to sequence at least all of the open reading frames of genomes in cancer patients, comparing the sequences derived from tumor to those from normal tissue. The results of these studies are, at this point, unclear.
It would be extremely helpful to be able to formulate a treatment protocol for an individual patient based on the vulnerability of tumor cells in this patient to such a protocol as determined by the pathway-based irregularities which appear to be associated with the tumor. The present invention offers just such an opportunity.
The invention solves the problem of tailoring treatment protocols to individual cancer patients in a rational way by assessing the abnormalities effecting malignant growth in the tumor cells of the individual. By ascertaining the abnormalities in tumor cells as opposed to normal cells in the same individual, these abnormalities can be targeted in view of the availability of the many drugs whose target sites are already known.
Thus, in one aspect, the invention is directed to a method to identify a treatment target protocol in an individual cancer patient, which method comprises:
(a) ascertaining characteristics of the genome and/or characteristics of the molecular phenotype in a biopsy of the cancer afflicting said patient to obtain one or more first data sets and in normal tissue of said patient to obtain one or more second data sets;
(b) identifying differentiated characteristics in said one or more first data sets which differ from those in said one or more second data sets;
(c) ascertaining one or more pathways associated with said identified differentiated characteristics; and
(d) identifying at least one therapeutic target associated with said one or more pathways.
The method may further include designing a treatment protocol using drugs and/or biologics that interact with said at least one target.
The invention may further include administering the formulated treatment protocol to the patient and repeating the process after administration to determine whether the treatment had an impact and/or caused redundant pathways to operate. In addition, the treatment protocol formulated according to the method of the invention, may, in cooperation with the identified differentiated characteristics, be applied to discover further therapeutic and diagnostic methods appropriate for additional subjects.
In determining data sets related to the genome, among those characteristics that will be assessed are: presence of single nucleotide polymorphisms (SNPs), loss of heterozygosity (LOH), copy number variants (CNVs) and gene methylation, and sequence (full genome, full exome, or targeted). Multiple polymorphisms would, of course, also be included.
Characteristics which provide datasets for molecular phenotypes include overexpression or underexpression of open reading frames assessed either by RNA level or protein levels, and proteomic and activity analyses.
It is advantageous to diminish the misleading effects of noise in a single type of dataset by triangulating multiple data points to identify a biologically significant pattern (e.g., a pattern of over- and under-expressed genes consistent with the dysregulation of a specific pathway). It is often possible, as well, to extrapolate the results to characteristics that are not themselves measured, as illustrated below.
While it is possible to perform the method of the invention using only one type of characteristic or data set, such as over/underexpression of open reading frames, it is highly advantageous to use multiple types of data sets so that integration analyses can be performed taking advantage of redundancy of indications. Thus, using combinations of, for example, overexpression/underexpression with CNV/LOH databases not only provides an increased level of confidence in the results, but also allows correlations to come to light leading to hypothesize pathways that might not otherwise be seen.
The method of the invention takes advantage of the exponential growth of knowledge related to gene expression, metabolic pathways, signal transduction pathways, the nature of drug targets, and cell regulation systems that has accumulated over the past half century, as well as techniques for organizing this information, and accessing multiplicities of data points in a relatively economic fashion. A number of canonical pathways are already postulated and understood in the art based on isolated findings reported in the literature, but many more pathways remain to be elucidated by assembling and evaluating these data. By virtue of obtaining and triangulating large numbers of data points for an individual patient within and across datasets, applicants are able to overcome the inherent noise from a measurement of few samples and provide a road map of the abnormalities associated with tumor cells as opposed to normal cells in the patient and thus formulate treatment regimens targeting the components of these irregularities.
The method of the invention begins by obtaining suitable biopsy samples from the patient. The biopsy is obtained using standard methodology; at least one sample, but preferably three (which allows calculation of a p-value), is obtained from tumor tissue and another sample but preferably three, is obtained from normal tissue in the same individual. The individual may have a primary solid tumor of an organ such as liver, kidney, stomach, bone, lung, pancreas, prostate, breast, ovary, bladder, eye, skin, or other specific location whereby an intact sample of small dimension can be obtained readily. Alternatively, the tumor cell may be distributed throughout the bloodstream as is the case for leukemias or lymphomas and the tumor cells are then recovered from and separated from normal cells in the blood. Tumor markers have also been found in bodily fluids in general such as saliva and serum. The primary solid tumor may also have metastasized and samples of metastatic tissue should also be obtained and assayed according to the method of the invention. In one embodiment, normal cells are obtained from similar tissues in which the primary tumor is found in the same individual who provides the tumor sample. Alternatively, normal tissue from the organ to which it has metastasized could be used as the comparative standard. If normal tissue from the same patient is not available, expression levels of various genes in normal tissues are available from databases such as GEO, maintained by the NCBI, as well as several maintained by companies (e.g., Gene Logic). One advantage of using the patient's own normal tissue is that the standard permits taking account of any drugs that may be in the system of this patient, such as chemotherapeutic drugs that have already been administered, as well as individual biological variability.
In some cases, the normal tissue may contain substantial numbers of stromal cells which are cells that support the tumor itself and which may distort the comparison. These stromal cells are connective tissue cells and are associated with a number of organs to support the functional cells in bodily organs. Stromal cells include fibroblasts, immune cells and endothelial cells, among others. In particular, if the normal tissue contains stromal cells, the methods described below to further validate the results of the method are particularly important.
The biopsy samples are preserved by methods standard in the art, such as flash-freezing or by embedding them in paraffin and fixing them with formalin.
Next, the relevant cellular components are extracted. For analysis of a molecular phenotype, expression levels may be measured using, for example, level of mRNA. The mRNA is extracted and isolated using standard techniques. The extracted mRNA is then assessed using whole genome microarrays, many of which are commercially available from Affymetrix, Illumina, or Agilent, for example, or is obtained through whole exome sequencing. The results from the microarray analysis provide data which show expression levels of a multiplicity of genes in tumor derived tissue and normal tissue.
Comparison for each gene of its level of expression in tumor and normal tissue permits an assessment of overexpression or underexpression of each gene in the tumor tissue as compared to normal. Any useable level of differentiation can be employed, for example, only genes that are expressed at a level of 10-fold, 5-fold, 3-fold or 2-fold differences may be considered. Within the same comparison, if desired, different differential standards may be employed for different groups of genes. It is not necessary to use hard cutoffs, and fadeout techniques can also be employed. Differing levels of p-values may also be employed to filter the gene list, depending on the context.
For metastasized tissue, the normal control may include either the primary organ or the organ which is the location of the metastasis; both can be employed if desired. Further, if there are multiple metastases, each one may have a different pattern.
In addition to assessing expression levels using mRNA as an index, the levels of protein present may be assessed using, for example, standard proteomic techniques or immunoassays. The activity of various proteins may also be assessed using standard techniques. The nature of the analysis of molecular phenotype is not critical—any method to obtain information about aspects of the phenotypic characteristics that may be relevant to the cancer could be used.
For determination of genomic characteristics, chromosomal tissue is extracted from the biopsy samples and analyzed, for example, for the presence of SNPs that are characteristic of the tumor tissue as compared to normal. The analysis also involves microarrays, and microarrays for SNP analysis are also available commercially. There may also be multiple copies of the certain genes and the commercially available SNP arrays are able to provide information concerning copy number (CNV), as well as the presence of the SNP itself. This information can also be obtained through full-genome sequencing, or other designed sequencing approaches.
The identification of one or more SNPs in the tumor tissue as compared to normal tissue, and the copy number thereof, as well as loss of heterozygosity (LOH) or methylation patterns also provide information as to the pathway irregularities that might be found in the tumor tissue. Multiplicities of SNPs provide further information along these lines. In one embodiment, information regarding copy number of genes in tumor tissue may be combined with the data on overexpressed and underexpressed genes to result in additional data points that add to the accuracy of the analysis. Thus, since a single patient is being evaluated, the availability of more data points provides an opportunity to distinguish signal from noise, by noting convergent results. Using, for example, the combination just of SNP data and expression data, there may be 20-50 or more data points supporting a given therapeutic hypothesis (pathway members×SNP/CNV).
As illustrated below, the data obtained are used as a basis for determining which pathways in the tumor cell are abnormal. The pathways may be metabolic pathways or signal transduction pathways or any other group of interacting proteins which may affect cellular functioning. The pathways may either be those already known and described in textbooks, or may be assembled from curating the primary literature. This curatorial activity and assembly into putative pathways has already been accomplished in many instances and algorithms for fitting aberrant genes, such as overexpressed and underexpressed genes, into such pathways are available from, for example, Ingenuity, GeneGo, and Pathway Assist. These algorithms are only available for expression data, so other types of data (copy number, mutation, etc.) are inserted into Ingenuity with a specific fold change—e.g., 1,000—used as a “flag” to identify them. The resultant data are then used for visualization purposes to identify pathway hypotheses, and are later removed and adjusted to be denoted by other means for the purpose of elucidating the pathways/discussing them. These tools may be supplemented by curatorial activities practiced by the diagnostician and assembled in the diagnostician's own database. This latter possibility is particularly important since SNPs occurring in tumors may not necessarily be represented in commercially available microarrays.
Clearly, the complexity of the correlations and algorithms required for determining relevant pathways requires the use of software and computer manipulations.
The dysregulated pathways identified using these techniques are assessed by several criteria, including
Of course, not every one of the above five criteria need be met. However, if a protocol is to be formulated, there must be known or investigational drugs or biologics that can target the components of the pathways identified.
A single type of data, such as overexpression/underexpression may be employed when necessary, but it is beneficial when possible to combine information concerning differential characteristics obtained according to these criteria with differentiated characteristics obtained using genomic information such as SNPs or CNV or both, or with other types of data sets, to that integration techniques can be employed.
The integration of more than one type of data is illustrated below in Examples 4 and 5. By integrating data from more than one type of determination (e.g., expression levels and genomic data) targets and treatments are suggested that would not have been evident by the use of one data set alone.
This latter confirmatory data employs analysis of either the function of the individual genes identified in the pathway or the genes that are simply on the list. If these genes are known to provide functions that are reasonably related to cell proliferation of abnormal growth, this further confirms the validity of the list and the projected pathway. If the pathway contains genes that exhibit SNPs in the tumor tissue and altered copy number, this further validates the relevance of the pathway.
Triangulation and Integration
As noted above, because multiple observations are obtained, their collective implications will permit deduction of the existence of characteristics that have not been directly measured. This can be considered as one manifestation of “triangulation” and/or “integration” which permits such inferences to be drawn.
For instance, where copy number and expression data indicate possible enhanced EGFR activity and thus hyperphosphorylation, this is inferred by data showing that the AT1R pathway was dysregulated. It is known that AT1R transactivates EGFR. It is inferred from an indication that EGFR is being degraded at a slower rate than normal, and the receptor not being desensitized as much as it would ordinarily be because p38 is downregulated, and one of its functions is to desensitize and degrade EGFR.
Thus, by correlating the results of multiple data points and multiple types of analysis, greater assurance is provided that the measured parameters are significant (as discussed further below) and further leads to additional conclusions that could not be drawn simply from the actual measured data points.
In addition, the noise level is managed by triangulation methods (as discussed in further detail below).
The term “triangulation” as used in the present application refers to assembling multiple individual items of data into meaningful compilations based on the known interrelationships of the components for which each data point is obtained. Thus, data with respect to individual members of a known pathway, for example, are assembled so that mutually supportive inferences enhance the probability that the pathway itself is aberrant or not aberrant in a tumor sample. By virtue of assembling these data in an orderly fashion, the significance level of the conclusion suggested by the data is enhanced. This is essentially a way to obtain meaningful results against a noisy background, as discussed further in the following paragraph and in the algorithms described below.
Management of the noise problem has traditionally been done by using a large cohort of patients/data/samples, and averaging them. While one may not trust every gene of every sample, the average is much more trustworthy. For example, when using microarrays, a significance value of p<0.05 for an individual gene is not particularly valuable. The reason is that there are 40,000 genes on the chip, so one would expect that 40,000×0.05=2,000 genes that would show as p<0.05 purely on a random basis. Typically a correction factor is used, multiplying the p value by the number of genes (“false discovery” or Bonferroni correction), so that one would need a p<0.00001 on a specific gene to have a 5% overall chance that the data are correct. Current technology does not provide such sensitivity for a specific sample, so the usual approach is to average over numerous data sets, to lower the p value to this threshold, as well as often to predefine a limited set of genes (e.g., a few hundred) that one is interested in to reduce the magnitude of the correction and lower the p-value. Even then it is hard to achieve significance, which means that further validation studies that focus on the single gene of interest are necessary to establish significance for it.
In another example, for microarray data with 20,000 known genes, to be certain that a given gene is statistically significant to the p=0.05 level requires a p-value on the individual genes of p=2×10−6. Obtaining p=0.05 on the single gene would require 1×1013 samples. One approach is to restrict the sample to a set of candidate genes, for example, to only 100 out of the 20,000. This would mean a p-value of only p=0.0005 for each gene corresponding to 10,000 samples. These calculations use the Bonferroni correction of p_adj=Np, where N is the number of genes being examined, and the standard error of the mean formula, p_mean=p/sqrt(n) where n is the number of samples.
The approach in the prior art is to use a set of samples for discovery of the gene, and then a set of samples used for validation, focused only on the gene of interest. As many genes will “appear” attractive on the training set, the odds of them validating in the validation set are low. Thus, these approaches work with larger databases, but not with only a few samples.
When working with a single patient, however, this cannot be done—the data are limited to a very small number of samples (e.g., 3 replicates), much too small to use this approach. Previous attempts to overcome this required a great deal of experimental/wetlab work to validate results for every gene of interest, and require use of a single platform. Changing arrays, or using FFPE samples instead of flash frozen, would require repeating all of these experiments.
The present invention achieves these goals by examining dysregulation at the functional/pathway level. If only the gene level is examined, it is important to know whether or not that gene is really overexpressed. At the pathway level, if there is activity among a set of 10-15 different genes, it matters less whether any one gene is really overexpressed. For example, in validating the results by IHC, IHC would not be needed on each of the specific genes that were upregulated to see if they, one by one, were upregulated. Rather, IHC is done on as few or as many elements of the pathway as desired/practicable, to see if a significant number of them were affected. Even if they were different pathway elements, the conclusion—that this pathway is activated—would be the same (provided, of course, that the data are broadly consistent—i.e., inhibition vs. activation should be the same).
This approach lowers the possible number of “discoveries” from the number of genes on the chip to the number of pathways that are possibly dysregulated leading to a possible treatment—a few hundred, instead of tens of thousands. One could ask questions like: if there is a pathway consisting of q elements, how many would have to be dysregulated at the p<0.05 level to have a p<0.05 that the pathway is activated? Algorithms that answer this question are discussed further below and are one of the ways (but only one) by which hypotheses are judged.
For example, to establish with 95% confidence that a pathway was activated by looking at several genes simultaneously, with only 3 samples, if the pathway had 10 elements only 5 of those would need to have 95% confidence; with 20 elements in the pathway, only 7; and with 40 elements, only 9. This calculation assumes approximately 283 different possible pathways (the number of canonical pathways in Ingenuity); as calculated by the statistical algorithm described below. Thus, by looking at pathways rather than individual genes one greatly reduces the number of possible discoveries, and therefore the multiple correction factor; this method requires several genes to be expressed simultaneously. The math works very similarly when triangulating with different technologies.
This method basically devalues the contribution of any one gene, so that inability to establish significance based on a single gene is not fatal. As shown by the statistical algorithm below—a single gene with an incredibly low p value or a large set of genes with moderately low p-values could both yield a significant result, but these are not needed for the invention methods to succeed. Instead of getting large amounts of data by looking at the same gene across a cohort, the present method provides data by looking at related genes within the same patient to achieve significance.
Three different algorithms have been developed to evaluate the significance of an identified pathway hypothesized on the basis of the analyzed data to furnish a target for a therapeutic to analyze them in 3 different ways. Modifications of these to account for any negative data are described as well.
Algorithm 1 is used, independent of the p values of specific data, to determine how many pathway elements must be dysregulated at the 95% confidence level (for instance) to have an overall 95% confidence that the pathway is active.
Algorithm 2 inputs the pathway data—the exact p-values of the various elements—and calculates a p-value of the overall pathway.
Algorithm 3 introduces the concept of “privileged data.” For instance, to conclude that the angiotensin pathway is activated, dysregulation of all pathway elements is helpful, but specifically dysregulation of angiotensin and its receptor are more helpful than other components, and this can be reflected in the statistics.
Algorithms 1 and 2 are focused on measuring strength of the hypothesis that the pathway is involved, while algorithm 3 adds biological reasoning that might be useful in identifying hypotheses that are likely to yield fruit in the real world. Additional elements of biological reasoning (such as the “privileged” element concept) can be added incrementally to determine whether they can reproduce the conclusions of a more subtle/sophisticated human interpreter, and if they don't, how further to augment them. Each refinement will add a tunable parameter, which will have to be determined experimentally, and so the more complicated the reasoning, the more data will be required to test/tune the parameter. Algorithms 1 and 2 have no tunable parameters (merely measuring), and algorithm 3 has one tunable parameter (beyond measurement, to prediction).
Π=1−{1−[Σk=nq(q|k)(1−p)(q−k)pk]}N Algorithm 1a
Where Π=probability that pathway is not noise, p is the threshold probability for a single gene (usually 0.05), n is the number of pathway elements that are dysregulated/amplified/mutated in a manner consistent with the hypothesis, q is the total number of elements in the pathway, N is the total number of possible pathways that might be considered, and (q|k) is q!/k!(q−k)!
Example of utility: If there are, say, 200 possible pathways to consider, then to get a 95% confidence level associated with the pathway activation (after correcting for multiple pathways) a pathway of 10 elements would require 6 of those elements be significant to p=0.05, a pathway of 20 elements would require 7, a pathway of 40 elements would require 9, etc.
Proof: We define Π as the probability that, for any one of N pathways, at least n elements out of a possible q have probability <p.
Let Π′ be the same as Π but with respect to a single pathway. Then:
Π=1−{1−Π′}N
which reduces to the normal Bonferroni correction for small Π′.
The probability of exactly k elements out of q being dysregulated with probability <p is given by the binomial expansion
(q|k)(1−p)(q−k)pk
and so Π′ is given as
Π′=Σk=nq(q|k)(1−p)(q−k)pk
leading to algorithm 1.
Π=1−{1−(q|n)(1−pT)(q−n)Πk=1npk}N Algorithm 2a
Where Π=probability that pathway is not noise, pT is the threshold probability for a single gene (usually 0.05), pk is the p-value for a given gene k, where the genes are ordered from lowest p-value to highest p-value, n is the number of pathway elements that are dysregulated/amplified/mutated, q is the total number of elements in the pathway, N is the total number of possible pathways that might be considered, and (q|n) is q!/n!(q−n)!
Example of utility: The previous formula just considers the case of p=0.05. However, if we have one or more genes with specific p-values that are much smaller, fewer genes may be required to conclude significance.
Proof: We define Π as the probability that the probability of a given gene <p for the specific n elements 1, . . . n, with probabilities p1, . . . pn.
As in algorithm 1a, let Π′ be the same as Π but with respect to a single pathway. Then:
Π=1−{1−Π′}N
Here, Π′ is the probability of exactly n elements out of q being dysregulated with the specific probabilities p1, . . . pn, all <pT while the other q−n elements have p>pT. This is given by
Π′=(q|n)(1−pT)(q−n)Πk=1npk
where the factor (q|n) derives from the fact that the n significant genes and the q−n non-significant genes could be reordered arbitrarily without changing the result.
Putting these two equations together leads to algorithm 2a.
Π=1−{1−[(q+μ|n+τ)(1−pT)(q−n)Πk=1npk/(μ|τ)](1−β)[(μ|τ)(1−pT)(μ−τ)Πr=1τpr]}N Algorithm 3a
Where Π=probability that pathway is activated, pT is the threshold probability for a single gene (usually 0.05), there are μ privileged elements, of which τ have p<pT and there are q non-privileged elements, of which n have p<pT. pk is the p-value for a non-privileged gene k, and similarly pr is the p-value for a privileged gene r. β is the degree of privilege, with 0≤β≤1, with β=0 meaning that there is no privilege (i.e., the elements μ have the same importance as the elements q), and conversely β=1 means that the privilege is complete, i.e., the q elements are essentially irrelevant to concluding whether the pathway is activated. N is the total number of possible pathways that might be considered, and (q|n) is q!/n!(q−n)!
Example of utility: In cases where the VEGF pathway appears to be activated (in the sense that the data is inconsistent with the null hypothesis of the data being noise) as determined by algorithms 1a and/or 2a. Biologically, however, a hypothesis in which the VEGF ligand and receptor are specifically dysregulated will carry more weight than those where more ancillary genes are dysregulated, though the ancillary genes are still important to concluding the relevance of the pathway. In this case, μ=2 (the two privileged genes) and β represents the magnitude of the relative importance of these genes over the others.
Rationale: Using similar logic as for algorithm 2a, we can define the probability that the patterns seen in the privileged genes are noise by
[(μ|τ)(1−pT)(μ−τ)Πr=1τpr]
which should be the result for β=1 (only privileged genes matter), where for β=0 (privileged genes and non-privileged are of equal importance), the result should reduce to:
(q+μ|n+τ)(1−pT)(q+μ−n−τ)Πk=1npkΠr=1τpr
i.e., the same result as if we had never selected out the privileged subgroup μ. The algorithm above reduces to these two extreme cases directly and has other critical properties such as decreasing the contribution of the non-privileged genes monotonically as β increases, being a continuous function of β, etc. Since “degree of privilege” has no independent definition between 0 and 1, any other formula that has these properties can be made equivalent to this one by reparametrizing β. Since the “correct” value for β will have to be measured experimentally in order to most closely reproduce human judgment about the importance of this privilege, all parametrizations of β are of equal value and therefore the formula above represents a correct approach to capturing this level of judgment. This particular parametrization has the property that the log of the probability is a linear function of β, interpolating between the two extremal results.
Even a formula that is non-monotonic in β would technically still be acceptable if β is tuned experimentally, but the results would be more difficult to interpret/understand on an intuitive level.
In the event that there are negative data (i.e., contradictory data that meet the standard of significance, usually 0.05), these algorithms are modified to take this into account, as set forth in algorithms 1b, 2b, and 3b.
To account for negative data, the above algorithms should be modified to
Π=1−{1−Π+/(Π++Π−−Π+Π−)}N
where, Π+ and Π− are defined in the following algorithm-specific ways:
Π+=[Σk=nq(q|k)(1−p)(q−k)pk] and Π−=[Σk=n′q(q|k)(1−p)(q−k)pk] Algorithm 1b
where n′ is the number of statistically significant aberrant genes that are evidence against the hypothesis and all other definitions are as in Algorithm 1a.
Π+=(q|n)(1−pT)(q−n)Πk=1npk and Π−=(q|n′)(1−pT)(q−n′)Πk=q−n′qpk Algorithm 2b
where n′ is defined as in Algorithm 1b, all other definitions are as in Algorithm 2a, and the genes are ordered so that the first n genes are those statistically that are statistically significant in support of the hypothesis, the next q−n−n′ genes that have no statistically significant measurement are next, and finally the n′ genes that have evidence against the hypothesis are listed.
Π+=[(q+μ|n+τ)(1−pT)(q−n)Πk=1npk/(μ|τ)](1−β)[(μ|τ)(1−pT)(μ−τ)Πr=1τpr] and
Π−=[(q+μ|n+τ′)(1−pT)(q−n′)Πk=1n′pk/(μ|τ′)](1−β)[(μ|τ′)(1−pT)(μ−τ′)Πr=1τ′pr] Algorithm 3b
where n′ the number of negative data points among the non-privileged genes, τ′ is the number of negative data points among the privileged genes, and all other definitions are as in Algorithm 3a.
Proof:
In the case of each of these three algorithms, Π+ represents the probability of the hypothesis being false (ignoring the negative data points) while Π− represents the probability of the opposite hypothesis being false (ignoring the positive data points), as per the proofs in each of the 3 previous cases. H+ and H− are defined as the probabilities of the hypothesis being true and the opposite hypothesis being true, respectively. There are four hypotheses that capture the possible space:
By Bayes' rule, the probability of each hypothesis being true, given the data observed, is
P(Hi|data)=P(data|Hi)P(Hi)/Z, where Z=Σj=14P(data|Hj)P(Hj).
Under the null hypothesis (that the data are simply noise), the data sets are independent, so we have
P(H1)=(1−Π+)Π−
P(H2)=Π+(1−Π−)
P(H3)=Π+Π−
P(H4)=(1−Π+)(1−Π−)
Usually, P(data|Hi)=1, because the data were observed, in which case the Bayes' rule above reduces to the tautology
P(Hi|data)=P(Hi)/Z, where Z=Σi=14P(Hi)=1.
However, since H4 is not internally consistent (two hypotheses contradicting each other cannot formally be true),
P(data|H4)=0.
Then Bayes' rule reduces to:
P(Hi|data)=P(Hi)/Z, where Z=Σj=13P(Hj) for i=1, 2, or 3, and P(H4|data)=0
or specifically, for i=1, plugging in the above gives:
P(H1)=(1−Π+)Π−/(Π++Π−−Π+Π−).
Since the p-value sought is the probability that H1 is false, before correcting for multiple pathways this is
1−P(H1)=Π+/(Π++Π−−Π+Π−).
Applying the correction for multiple pathways as in the previous proofs yields the algorithm.
In addition to validating the pathway hypothesized by triangulation as determined using the algorithms above, “integration” also allows more meaningful appreciation of real results against a noisy background, but rather than applying the algorithms to a coherent data set, combines data from multiple technologies, that aren't necessarily used to “playing together”, so that they can be viewed and considered simultaneously within the functional biology. For example, the formats for the CNV data are very different from those of the expression data; mutation data are yet different from either. To conclude, “the expression says this, the copy number says that, and they are or are not consistent,” is straightforward. It is more difficult to perceive that “Here is something that wouldn't have caught my attention if I were looking individually at either of the data sets, but when looking at them together in the same picture, I see it clearly.” For one patient, for instance, before the data were integrated, no hypotheses were found. After building the tools (such as assigning arbitrary values for results in one data set into calculations designed for another data set as described in paragraph [0042]) to integrate them, three good hypotheses emerged. Integration thus has a technical (IT) component and a strategic component to it.
Results
The data and results obtained from an individual patient reveal the relevant disease biology, ties the biology to drugs, and these drugs are tested in the patient. For drugs that work in the individual patient, other patients who suffer that cancer or other cancers, and have the same critical elements are likely to respond the same way to the therapy. A validation study is done to confirm this.
Thus, drugs may include small molecules—e.g., conventional drugs such as Gleevec®, doxorubicin, carboplatin and the like, including investigational drugs. Biologics such as antibodies, therapeutic proteins, gene therapy systems, stem cell therapy systems, and the like, including those in investigational studies may also be used. Use of antibody therapy in tumor treatment is becoming conventional, as is treatment with cytokines and other proteins. Our approach has the ability to exploit the entire pharmacopeia of ˜2500 approved drugs as well as investigational agents currently in trials. to engineer an effective customized treatment.
Using the dysregulated pathway information, treatment protocols using drugs or biologics are then proposed and formulated. A database of compounds/biologics/therapies is maintained together with known and suspected targets so that these can be compared to the pathways to determine which protocols are most effective. This is particularly useful in proposing combination therapies, as multiple components of the pathway may be targeted by a multiplicity of components of the protocol.
By utilizing the analysis described herein, the probability of success in treating an individual patient is greatly improved, and the formulated protocol may then be administered to the patient. Routine optimization will determine mode of administration, timing, dosage levels and other parameters relevant to the particular treatment.
In some cases, additional validation studies may be suggested to provide further evidence for the explicated hypotheses. These may include, but not be limited to, studies to assess phosphorylation or other mode of activation of cellular proteins, assessment of mutation status of individual genes, screening of drugs against tumor-derived cells, or various other cell or molecular biology-based assays.
While intuitively it would seem better to analyze a database to look for appropriate targets, that may not be the case. As there are often hundreds of subtypes within a given cancer type, and the search on the database will generally only give information either on the most prevalent subtypes or will give “high level” information. The methods of the present invention give information on rare subtypes, and very “fine-grained” information.
Patients on whom the invention methods are conducted may be those who have failed multiple lines of therapies that have been approved based on results in trials—which by definition focus on the prevalent subtypes, and thus are likely to have rare subtypes. Diagnostics relevant to a rare subtype can be as valuable as those relevant to a common subtype; for example, cKIT mutation in melanoma is only present in 3% of melanomas, but when it is present, Gleevec® is highly effective, so all melanoma patients should be tested for cKIT despite its relative rarity.
The distinction between “high level” vs. “fine grained” information can best be understood by the following example. One distinction among colon cancer patients is whether they have a mutated or wild-type EGFR. This was a test originally used to predict responsiveness to Erbitux™ (two “subtypes”). Later studies revealed that a mutated KRAS predicted a poor responsiveness to Erbitux™ whether or not EGFR is mutated. Both tests are now used in combination (or KRAS alone) to determine susceptibility to Erbitux™. Two subtypes have thus now been split into more. 70% of patients with EGFR mutant, KRAS wild-type, respond to EGFR inhibitors. So there is yet another reason why this is not 100% to be discovered in the future. This will split this subtype into 2 (or more) again where one is yet further enriched for EGFR inhibition response. So, there may be 100 subtypes, with this representing 10 of them, for illustration. The present method, nucleating around a single case rather than a database search, is more likely to distinguish a single subtype from the other 99 rather than a higher-level grouping. In principle, our approach can be used to discover clinically significant subtypes, such as EGFR mutant cancers with mutated KRAS, in a single individual. If validated in other patients, these new subtypes can become valuable additions to the high level databases and standard panels of point mutations used to stratify patients.
The following examples are offered to illustrate but not to limit the invention.
Colon tumor tissue and colon normal tissue, as well as tissue from a liver metastasis from an individual patient were biopsied and subjected to Affymetrix transcription profiling. Ratios of gene expression (mRNA levels) from the primary colon tumor and liver metastasis samples, both relative to normal colon tissue samples were determined. Genes with an expression ratio threshold of 1.8-fold up- or down-regulation, and a significance P-value of 0.05 in malignant relative to normal cells were identified as 288 genes from the colon tumor and 348 genes from the liver metastasis.
Using a tool provided by Ingenuity Systems, the identified genes were subjected to an algorithm which finds highly interconnected networks of interacting genes (and their corresponding proteins). Ingenuity's algorithm for constructing a network graph is based on hand-curating protein/protein interactions (as defined by physical and/or functional interactions) directly from the research literature. In each individual analysis, the Ingenuity algorithm compares the regulated genes to this underlying master network and clusters of proteins that have multiple mutual interactions are identified and presented as smaller sub-networks. The resulting pathways can be directly supported by references to the literature both within the Ingenuity tool, and independently. [Similar algorithms are in use by other tools (e.g., those by GeneGo, and one in the public domain); we use Ingenuity because their database of curated literature is currently the most comprehensive, but the work is not conditioned on them specifically.] These findings were then further analyzed independently of the Ingenuity tool, to find particularly relevant pathways which could provide potential therapeutic targets.
An initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for neoplasms of this type. The networks that were assembled by the protein interaction algorithm from the list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. From the primary colon tumor sample, the four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:
Cancer
Cellular Movement
Cell Morphology
Neurological Disease
Protein Synthesis
Connective Tissue Disorders
Gastrointestinal Disease
Cell Signaling
Carbohydrate Metabolism
Molecular Transport
Small Molecule Biochemistry
From the liver metastasis sample, the four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:
Genetic Disorder
Hematological Disease
Ophthalmic Disease
Lipid Metabolism
Small Molecule Biochemistry
Protein Synthesis
Hematological System Development
Organismal Functions
and Function
Infectious Disease
Post-Translational Modification
Protein Folding
Cancer
This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue. This helps to confirm that the differently expressed genes are from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions, from both samples, is set forth at the end of this Example.
Both entire lists of differently expressed genes were scored for associated cellular functions. In the primary tumor, the highest scoring category was cancer. (
A similar analysis of the cellular functions associated with the individual dysregulated genes from the liver metastasis is shown below (
The network analysis yielded several findings of note. A first attempt was made to find pathways that were dysregulated in both the primary tumor and the liver metastasis in the hopes of targeting both sites. Following this strategy, two networks with similar features in both tumors were identified (
However, it may be possible to indirectly target the Hsp pathways by inhibiting a family of proteins that interacts with them, the histone deacetylases (HDACs). HDACs were first identified as enzymes which deacetylated histone proteins, but more recently have been shown to have a wider array of substrates. One member, HDAC2 is up-regulated in the primary tumor and expressed in the liver metastasis. Other family members, including HDAC6 are expressed in both the primary tumor and the metastasis sample. It has been shown in the literature that HDAC inhibition is pro-apoptotic and has anti-tumor properties, through several mechanisms. One of these mechanisms is via hyperacetylation, and hence deactivation, of members of the Hsp family by HDAC6. In particular, Hsp90, which is up-regulated in both primary tumor and metastasis, has been shown to be deactivated by HDAC inhibition.
An additional finding was that in the liver metastasis, the receptor tyrosine kinase, RET, and its surrounding pathway, were up-regulated (see
The patient might be treated by inhibiting HDAC2 which should target both the primary tumor and the liver metastasis via deactivation of Hsp and other antitumor properties of HDAC inhibition. It is also possible to target liver metastases using sunitinib Sutent® to inhibit RET.
TABLE 1
Regulated Networks from Primary Colon Tumor Sample
Focus
ID
Molecules in Network
Score
Molecules
Top Functions
1
BHLHB2, COL1A2, ERK, GOPC, HSP90B1, IL6ST,
50
28
Cancer, Cellular
ITGAV, LOX, LUM, LYZ, MRLC2, NEXN, Pak,
Movement, Cell
PDGF BB, POSTN, PSMA3, PSMD6, RAB1A,
Morphology
RAD23B, RHOA, Rock, ROCK1, S100P, SFRS10,
SH3PXD2A, SKIL, SNRPF, SNRPG, SPARC, SUB1,
TFIIH, Tgf beta, Ubiquitin, VCAN, VIM
2
ANXA2, C3-Cfb, CAPZA1, CD55, CFH, CFI, DAD1,
50
28
Neurological Disease,
DDX5, EIF3E, EIF3H, FSH, G0S2, GJB2, GTF3A,
Protein Synthesis,
hCG, HSPH1, LDHA, LDHB, LDL, MCL1, NFkB,
Connective Tissue Disorders
PAIP2, PAWR, PLC, PTP4A1, RAB2A, RPL15,
RPL39, RPS3A, S100A10, SERPINB5, TCP1, TPT1
(includes EG: 7178), UBE2K, Vegf
3
Actin, Akt, ATP2A2, Calmodulin, CALU, Caspase,
40
24
Cancer, Gastrointestinal
CEACAM6 (includes EG: 4680), CLIC4, CSTB, DEK,
Disease, Cell Signaling
DSTN, Dynamin, ERH, HDAC2, HIF1A, HLA-
DQA1, Hsp70, Hsp90, HSPA5, HSPA8, Jnk,
MAP4K5, PAPOLA, PFN1, PI3K, PIK3C2A, PLOD2,
Proteasome, RGS1, RNA polymerase II, S100A11
(includes EG: 6282), TCEB1, TMF1, TXNDC17,
YWHAE
4
C15ORF15, D-glucose, EWSR1, FGA, Histone h3,
28
16
Carbohydrate Metabolism,
HNF4A, HOOK3, KRR1, LAPTM4A, MRPL33,
Molecular Transport, Small
MRPS18C, N4BP2L2, PDE4DIP, SEC31A, SMAD4,
Molecule Biochemistry
SSBP1, TBC1D16, THYN1, TINP1, TM9SF2,
USMG5, VPS29
5
ACTA2 (includes EG: 59), ALP, Ap1, Arp2/3,
27
18
Cell-To-Cell Signaling and
ATP5E, BMPR2, C3ORF10, CALD1, CSNK1A1,
Interaction, Cellular
DCN, F Actin, FN1, GJA1, Histone h3, IFI16, IK,
Assembly and Organization,
IL12, Insulin, Interferon alpha, KIAA0265, Mapk,
Cellular Movement
Mmp, NCKAP1, Nfat, P38 MAPK, Pdgf, Pka, Pkc(s),
PPIC, Rac, RAC1, S100A4, Scf, SPP1, TIMP2
6
ACTA2 (includes EG: 59), ANKH, BHLHB2, BIRC6,
27
18
Protein Degradation,
CCNK, CGGBP1, CHD3, COMMD6, COMMD1
Protein Synthesis, Viral
(includes EG: 150684), FMR1, H2AFZ, HIPK1,
Function
MAT2A, MCL1, NFIL3, NXF1, PDGF BB, PPA1,
PRPF40A, PRRX2, RELA, RFFL, RNF25, SFRS10,
SON, THOC7, TP53, TTC3, UBE2A, UBE2B,
UBE2D2, UBE2D3, UBQLN2, UBR3, WBP5
7
ACTA2 (includes EG: 59), BET1, CCDC90B,
25
17
Cellular Assembly and
CDK5RAP3 (includes EG: 80279), CISD1, COPB2,
Organization, Cell
COPZ1, COX7A2, CREBL1, CSDE1, HNF4A,
Morphology, Connective
HSPA4L, MCF2, MITD1, NFYB, PHF5A, PI4KB,
Tissue Development and
PRDX5, PRKAB1, RAB10, RAB11A, RTCD1,
Function
SCFD1, SF3B1, SF3B4, SF3B14, SYTL3, TAX1BP1,
TBC1D17, TMEM93, TSNAX, UBE2D3, UFM1,
USP5, YKT6
8
ATL3, ATP6V1B2, CLK4, CPNE1, CSNK1A1,
23
16
Cell-To-Cell Signaling and
CTGF, DDX1, DHX15, EIF5B, FAP, GCC2, HAT1,
Interaction, Cancer, Cellular
HEBP2, ITGA1, ITGA3, ITGAE, KITLG (includes
Growth and Proliferation
EG: 4254), L-1-tyrosine, LOC100128060, MIA,
MYCN, PDCD6, RELN, retinoic acid, RPL19, RPS19,
RPS23, RPS17 (includes EG: 6218), SLC38A2,
SNAI2, SPARC, TBC1D8, TGFBI, VEGFA, YWHAZ
9
BTF3 (includes EG: 689), C12ORF35, Ck2, DDX17,
21
15
RNA Post-Transcriptional
DICER1, HMGN1, HNRNPA1, HNRNPA2B1,
Modification, Cellular
HNRNPC, LSM2, LSM3, LSM4, LSM5, LSM7,
Assembly and Organization,
LSM8, LSM6 (includes EG: 11157), MED21,
Cellular Compromise
NAP1L1, NONO, NOP5/NOP58, PICALM, PRPF8,
RPL37, SAFB, SART3, SFRS3, SFRS12, SIP1,
SMN2, SNRPA1, SNRPD1, SNRPD2, SNRPD3,
SNRPE, SSB
10
BCAS1, beta-estradiol, BNIP3 (includes EG: 664),
21
15
Cancer, Cellular Growth
CCNA1, CCNF, CDR1, CHMP2B, CKS1B, DYNLL1,
and Proliferation,
ERBB2, GLB1, HTRA1, hydrogen peroxide, IRF6,
Reproductive System
KRT81 (includes EG: 3887), LARP5, NUCKS1,
Disease
PIK3C2A, PKIA, PKIB, PRDX5, PRDX6, PRSS23,
PTP4A2, RAB9A, S100A2, SKP1, SPARC, SSR1,
SSR3, TERT, thiobarbituric acid reactive substances,
WWC1, ZBTB7A, ZNF638
11
ANXA4, ASPN, CCL18, CCL19, CD40LG,
21
15
Cell-To-Cell Signaling and
chondroitin sulfate, CNN3, CSDA, CTSH, CXCL2,
Interaction, Hematological
DDX5, EEA1, EGLN1, FSCN1, HLA-DQA1, IFNG,
System Development and
ITGB7, KDELR2, KIF2A, LOX, MARCKSL1,
Function, Immune and
METAP2 (includes EG: 10988), MMP11, MYC,
Lymphatic System
NAPG, NEDD9, NSF, PLS3, POMP, RPS15A,
Development and Function
SAMD9, SERINC3, TGFB1, WISP1, YME1L1
12
ARHGEF18, BRCA1, BTF3 (includes EG: 689),
21
15
Protein Synthesis, Post-
CMTM6, CSDE1, CUL2, E3 RING, FSCN1, GH1,
Translational Modification,
MCC, MRPL36, NOVA1, NPEPPS, PDCD5, RHPN2,
Cellular Assembly and
RNF126, RPL35, SEC62, SEC63, SEC61A1, SEC61B,
Organization
SEC61G, SEPT2, SEPT7, SEPT9, SEPT11, SF3B14,
TAGLN2, TBCA, TMCO1, TMED2, TRAF6,
TXNDC1, UBE2D3, VHL
13
4-phenylbutyric acid, ANGPT2, ARNT2, ATIC,
17
13
Cell Signaling, Molecular
C14ORF156, CD247, CD3E, CDV3, COL12A1,
Transport, Vitamin and
COL4A5, Cpla2, CUL2, CYBA, DEF6, EDIL3,
Mineral Metabolism
FCGR2C, GAB2, HNRPDL, KCNK3, LRRFIP1,
MAT2A, NASP, OAZ1, SEPP1, SIRPA, SNRP70,
spermine, TAX1BP1, TNF, TNFRSF9, TRA2A,
TRAF6, UACA, YPEL5, ZAP70
14
AATF, ATN1, CCDC99, CDKN1A, CTCF,
15
12
Gene Expression, Cell
CYP27B1, E2F1, E2F6, EFEMP1, EIF1, EPC1,
Cycle, Connective Tissue
FBLN2, FEN1, HIST2H2AA3, IL2, ITM2B, KRAS,
Development and Function
MEGF8, NAB1, NAB2, PCSK5, PTPRJ, PTRH2,
REEP5, RERE, RPA3, RYBP, S100A4, Scf, SNF8,
SP2, TFDP2, TSG101, VPS36, ZBED5
15
C7ORF43, TMEM50A
2
1
16
GPI, PARP14
2
1
Cancer, Cell Morphology,
Cell-To-Cell Signaling and
Interaction
17
ENY2, MCM3AP
2
1
Gene Expression, DNA
Replication, Recombination,
and Repair, Molecular
Transport
18
C1GALT1, Glycoprotein-N-acetylgalactosamine 3-
2
1
Cardiovascular System
beta-galactosyltransferase
Development and Function,
Cell-To-Cell Signaling and
Interaction, Connective
Tissue Development and
Function
19
C21ORF66, GRIN1
2
1
Cardiovascular Disease,
Cell Death, Cell-To-Cell
Signaling and Interaction
20
DUB, USP34
2
1
21
DNAJC, DNAJC15, Hsp22/Hsp40/Hsp90
2
1
Carbohydrate Metabolism,
Drug Metabolism,
Molecular Transport
22
NADH2 dehydrogenase, NADH2 dehydrogenase
2
1
Cancer, Gastrointestinal
(ubiquinone), NDUFC2
Disease
TABLE 2
Regulated Networks from Liver Metastasis Sample
Focus
ID
Molecules in Network
Score
Molecules
Top Functions
1
ABCA6, APCS, APOC1, APOC3, C3-Cfb, CFB, CFH,
47
29
Genetic Disorder,
CFI, CP, CPB2, CTSL1, ERK, FG, FGA, FGB, FGG,
Hematological Disease,
Fibrin, FTL, GDI2, HP, HRG, HSBP1, Mmp, NAMPT,
Ophthalmic Disease
NTN4, QDPR, RAB1A, RAB8A, S100P, SAA4,
SERPIND1, SSBP1, Stat3-Stat3, TFF2, TTR
2
ABCD3, AKR1C4, C14ORF156, Ck2, CREBL2,
45
27
Lipid Metabolism, Small
DYNLL1, E2f, EIF5, EIF3H, EIF3J, FSH, Histone h3,
Molecule Biochemistry,
HLA-DQA1, HNRNPA1, HNRNPA2B1, HSPC152,
Protein Synthesis
IFITM2, IFITM3, IL12, Interferon alpha, ITM2B,
KIAA0265, LDHA, MHC Class II, MT1G, NONO,
PARP9, PGRMC1, PHYH, PTP4A1, RNA polymerase
II, SC4MOL, SFRS5, SLC27A2, TFF1
3
APOA2, APOB, APOF, B2M, C5, C6, C7, C1q, C1R,
41
26
Hematological System
C5-C6-C7, C5-C6-C7-C8, C8B, CALR, Complement
Development and Function,
component 1, CXCL16, DAD1, F5, F9, G0S2, HAMP,
Organismal Functions,
HDL, HSP90B1, IgG, LMAN1, MHC Class I, NFkB,
Infectious Disease
P4HB, PDIA3, PRDX4, PROS1, SAA@, SERPINC1,
SERPING1, TFPI, WTAP
4
14-3-3, ATP2A2, CSTB, DNAJA1, ERH, HIF1A,
40
25
Post-Translational
HSP, Hsp70, Hsp90, HSP90AA1, HSPA5, HSPA8,
Modification, Protein
IFN Beta, IL6ST, LYZ, Mapk, NSMCE1, PAPOLA,
Folding, Cancer
PFN1, PI3K, PPIA, PRDX6, Proteasome, PSMA3,
PSMB2, PSMD6, RDX, RET, S100A11 (includes
EG: 6282), SOD1, STAT, TCEB2, TEGT, Ubiquitin,
YWHAE
5
ACADM, ACAT1, Akt, ALB, ALDOB, AMPK,
38
24
Energy Production, Nucleic
ANGPTL3, APOH, ATP5E, ATP5J2, ATP5L,
Acid Metabolism, Small
COX5A, COX6C, COX7B, COX7C, CYB5A,
Molecule Biochemistry
Cytochrome c oxidase, H+-transporting two-sector
ATPase, Hnf3, Insulin, LEPR, NADH dehydrogenase,
NADH2 dehydrogenase, NADH2 dehydrogenase
(ubiquinone), NDUFA1, NDUFA2, NDUFA4,
NDUFAB1, NDUFB6, SCD, SLC2A2, TAT, Tcf 1/3/4,
TMBIM4, Vegf
6
A2M, ADFP, ALDH1A1, BNIP3 (includes EG: 664),
33
22
Small Molecule
CAR ligand-CAR-Retinoic acid-RXR&alpha, CAT,
Biochemistry, Drug
CD14, CYP2B6 (includes EG: 1555), CYP2C8,
Metabolism, Endocrine
CYP2C19, CYP2E1, CYP3A5, FABP1, GST, GSTO1,
System Development and
HSPE1, IGFBP1, Jnk, MGST2, MT1F, NADPH
Function
oxidase, Ncoa-Nr1i2-Rxra, Ncoa-Nr1i3-Rxra, P38
MAPK, PDGF BB, PXR ligand-PXR-Retinoic acid-
RXR&alpha, Rxr, SAT1, SERPINA2, SNRPG,
Trypsin, TXNDC17, UGT2B4, Unspecific
monooxygenase, VitaminD3-VDR-RXR
7
C12ORF62, CEBPB, CWC15, HNF1A, HNF4A,
27
16
Gene Expression, Cellular
HSDL2, LAPTM4A, MRP63, MRPL33, MRPL51,
Development, Hepatic
MRPS28, MT1X, N4BP2L2, ONECUT1, SEC11C,
System Development and
SLC38A4, TINP1, TM4SF4, TMEM123, TMEM176A
Function
8
ALB, AOX1, ATXN2, BNIP3 (includes EG: 664),
24
17
Cellular Assembly and
CYC1, CYTB, DAD1, FGF2, FTH1, Gsk3, hemin,
Organization, Lipid
iron, ITM2B, KRAS, LCP1, LRAT, MAPK9, MYCN,
Metabolism, Molecular
PLS3, retinoic acid, RPS7, SERPINA7, SLC38A2,
Transport
SPINK1 (includes EG: 6690), TMED2, Ubiquinol-
cytochrome-c reductase, UQCR, UQCRB, UQCRC2,
UQCRFS1, UQCRFSL1, UQCRH, UQCRQ, VHL
9
ANXA2, Ap1, ARHGEF12, Calpain, Cpla2, F2, F
23
17
Cell-To-Cell Signaling and
Actin, Filamin, FN1, GJB2, hCG, IL1, LAMP2, LDL,
Interaction, Cellular
MT2A, PAH, Pak, Pdgf, Pkc(s), PLC, Pld, PP2A,
Assembly and Organization,
Rap1, Ras homolog, RHOA, Rock, S100A10, SAR1B,
Cancer
SMARCA1, SPP1, ST6GAL1, SUB1, Tgf beta,
TIMP1, UBE2K
10
ACAA2, AIP, AKR1C4, BAAT, BET1, BRD4,
23
17
Lipid Metabolism, Small
C6ORF203, CCDC45, CLDN1, CLDN3, FOXA2,
Molecule Biochemistry,
GJB1, GOT1, HAO1, HNF4A, HSD17B4, INADL
Endocrine System Disorders
(includes EG: 10207), LSM3, LSM4, LSM5, LSM10,
MAL2, NR5A2, PBLD, SART3, SCFD1, SH3BGRL2,
SHFM1, SNRPD2, SNRPD3, SNRPE, STRAP, TDO2,
VPS29, YKT6
11
3-alpha,17-beta-androstanediol, 3-beta,17-beta-
23
17
Endocrine System
androstanediol, ALB, beta-estradiol, C11ORF10,
Development and Function,
CDH5, CFHR4, cholesterol, COMMD6, CPS1,
Small Molecule
CYB5A, CYP3A4, DDR1, Gsk3, HSD17B6, IDH1,
Biochemistry, Metabolic
IL12B, IRS2, ITGAM, LEAP2, LEP, MAPK9,
Disease
MMP12, MYBL1, PCSK1, PIK3C2A, PON3, RELA,
RPL36AL, SC4MOL, SC5DL, TBC1D8, TTPA,
UGP2, VEGFA
12
AADAC, ACADSB, ACTA2 (includes EG: 59),
20
15
Cell-To-Cell Signaling and
ADH4, ALB, AMBP, ANXA2, ASPN, BMP2, CDH11,
Interaction, Skeletal and
CHRDL2, CTNNB1, CTSS, CYC1, F13A1, FGF6,
Muscular System
GDF10, HNF1A, HRAS, ITGAE, KDELR2, LEO1,
Development and Function,
LGALS3, MAPK9, NR5A2, NUCB2, PCCA, PCSK6,
Tissue Development
phosphate, PZP, RPL37, RPS17 (includes EG: 6218),
TBCA, TGFB1, TTC1
13
ACTA2 (includes EG: 59), AFM, ALB, APP, AUH,
20
15
Organismal Injury and
CD4, CD40LG, CFHR5, Cu2+, CUGBP2, DECR1,
Abnormalities, Tissue
F12, GATM, hemin, heparin, HIST1H2BK, HPR,
Morphology, Cell-To-Cell
IFNG, IL4, IL13RA1, ITIH2, MAPK9, NFYB, POMP,
Signaling and Interaction
RNASE3, RRAGA, RRAGD, SEPP1, SERPINA5,
SERPINA6, SON, TFCP2, TMEM93, TNFAIP2,
UHRF1
14
ACTA2 (includes EG: 59), ALB, ARG1, ARRDC3,
18
14
Inflammatory Disease,
butyric acid, CARHSP1, CCL18, CD4, CDH11, CTSF,
Immunological Disease,
DDR1, DDT, G0S2, GC, Gsk3, GTF3A, HLA-DRA,
Viral Function
IFNGR2, IK, IL15, IL18R1, MAPK9, MMP12,
NNMT, NUP85, PAIP2, PIAS4, PRKRIR, PTP4A1,
SYAP1, SYNPO, TNF, TNFAIP2, TP53, Vegf
15
Actin, C5, C6, C7, C8, C9, C21ORF66, C5-C6-C7-C8,
16
13
Cell Death, Hematological
C5-C6-C7-C8-C9, C8A, C8B, C8G, Calmodulin,
Disease, Organismal Injury
CAMK2B, Caspase, CCL13, CD59, CIAO1, Cyp2b,
and Abnormalities
CYP2B7P1, Cytochrome c, dihydrotestosterone,
FAM96A, GRIN1, ICAM1, IFNA1, IRS2, MYO1B,
pyridine, RGN, RPS29, SEPP1
16
amino acids, ARL6IP1, ATM, CDC2L1 (includes
16
13
Cancer, Cell Death,
EG: 984), CDC45L, CDKN1A, CHD2, CTCF, CTDP1,
Hematological Disease
CTSK, E2F1, EIF1, GLUD1, GNPNAT1, Gsk3, HGF,
IL2, ITM2B, KIAA0999, LGALS3, MAP3K1,
MAPK9, MYC, PTRH2, Ras, RPL38 (includes
EG: 6169), RPS13 (includes EG: 6207), RPS15A,
RPS27L, Scf, TGFA, TPT1 (includes EG: 7178),
UBE2S, VCP, ZBED5
17
3-hydroxyisobutyryl-CoA hydrolase, HIBCH
2
1
18
ENY2, MCM3AP
2
1
Gene Expression, DNA
Replication, Recombination,
and Repair, Molecular
Transport
19
PCSK6, PRG4 (includes EG: 10216)
2
1
Cancer, Cell Morphology,
Cellular Development
20
BHMT2, TAL1
2
1
Cardiovascular System
Development and Function,
Embryonic Development,
Hematological System
Development and Function
21
IMP3, LPXN, MPHOSPH10
1
1
RNA Post-Transcriptional
Modification, Post-
Translational Modification,
Protein Synthesis
22
E2F6, EPC1, REEP5
1
1
Gene Expression, Cellular
Growth and Proliferation,
Cancer
23
DNAJC, DNAJC15, Hsp22/Hsp40/Hsp90
1
1
Carbohydrate Metabolism,
Drug Metabolism,
Molecular Transport
24
CIC, RHOJ, SEC62, SEC63
1
1
Cancer, Hepatic System
Disease, Protein Synthesis
Additional References
Biopsy samples from the patient's tumor and normal tissue were assayed for mRNA levels using Affymetrix transcription profiling. Genes with an expression ratio threshold of 3-fold up- or down-regulation, and a significance P-value of 0.05 yielded 4,519 unique genes.
Using a tool provided by Ingenuity Systems, the 4,519 genes were subjected to an algorithm which finds highly interconnected networks of interacting genes (and their corresponding proteins). Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways which could provide potential therapeutic targets or, if possible, clusters of interacting proteins which potentially could be targeted in combination for therapeutic benefit.
An initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. This serves as a crude measure of quality control for tissue handling and microarray processing methodology. The networks that were assembled by the protein interaction algorithm from the filtered list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The three top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:
Cancer
Cellular Assembly and Organization
Cellular Function and
RNA Post-Transcriptional Modification
Maintenance
Embryonic Development
Cell Cycle
This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue, and help to confirm that the data are from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is set forth at the end of this example.
The differentially expressed genes were also scored for associated cellular functions. The highest scoring category was Cancer. (
Network analysis of the Affymetrix data revealed up-regulation of many components of the PDGF pathway. Most notably, the receptor PDGFRα and two of its ligands, PDGFα and PDGFC were over-expressed. Notably, several downstream effectors of PDGFα/PDGFRα, specifically, STAT3 and PI3K, are also up-regulated, indicating dysregulation of this signaling pathway that is implicated in carcinogenesis (Andrae, J., et al., Genes & Dev. (2008) 22:1276). Increased PDGF signaling has been observed in several neoplastic conditions (Dai, C., et al., Genes & Dev. (2001) 15:1913-1925; Smith, J. S., et al., J. Neuropathol. Exp. Neurol. (2000) 59:495-503; Arai, H., supra; Zhao, J., et al., Genes Chromosomes Cancer (2002) 34:48-57). This may represent an attractive intervention target, as inhibition of the tyrosine kinase activity could dampen downstream activity in the pathway and possibly lessen the stimulatory effects of the pathway on cell proliferation and survival. See
The tyrosine kinase domain of PDGFRα is inhibited by imatinib (Gleevec®) and by sunitinib (Sutent®), which also targets VEGF. While the primary target of imatinib is the receptor tyrosine kinase c-ABL, it is also known to act at other targets including c-KIT and PDGFR (e.g., Wolf, D., et al., Curr. Cancer Drug Targets (2007) 7:251-258). Thus, regardless of the mutational status of c-KIT or c-ABL in this tumor, inhibition of PDGFRα may be considered by virtue of its potentially inhibitory effects on the up-regulated PDGF pathway.
The potential cellular mechanisms by which the PDGF pathway may promote tumor growth are manifold. As shown in
Also of note, VEGF was highly up-regulated (111-fold). To a lesser extent, its receptor and downstream effectors were up-regulated as well (
Our network analysis revealed that the PDGF pathway is strongly activated in the patient's samples, and has been shown in the literature to activate several mechanisms which directly and indirectly support tumor biology. Furthermore, there are FDA-approved therapies known to impact the PDGF pathway: imatinib (Gleevec®) and sunitinib (Sutent®).
Since there are negative data, algorithm 1b is applied to take account of two data points that are inconsistent with the hypothesis, the calculated probability of the pattern being produced only by chance (Π) is 4×10−7.
In this case, the total pathway elements (q)=15, which are 4 ligands (PDGF α, β, C, and D); 2 receptors (PDGFR α, β; 2 receptor inhibitors (Oav 1/3, GRB 2); 5 intermediaries before STAT (PKR, JAK1, JAK2, JAK3, SRC); and 2 STATs (STAT1, STAT3).
The total aberrant genes consistent with hypothesis (n) is 10, which are 2 ligands (PDGF α and C); 1 receptor (PDGFR α); 5 intermediaries before STAT (PKR, JAK1, JAK2, JAK3, SRC); and 2 STATs (STAT1, STAT3). The total aberrant genes inconsistent with the hypothesis (n′) is 2: 2 receptor inhibitors (Oav 1/3, GRB 2). The total number of possible pathways (N) is 283 canonical pathways in Ingenuity, the cut-off probability (p) is 0.05.
Then Π+=2×10−10, Π−=0.2 (these represent the “raw” probabilities of the hypothesis being false and the reverse hypothesis being false, respectively), leading to Π′=1×10−9 and further to Π=4×10−7 after the multiple pathway correction.
In algorithm 2b, inputting the specific p-values associated with the positive genes (ranging from 0.04 to 5×10−4) and those for the negative genes (0.01 and 1×10−4) leads to: Π+=2×10−19, Π−=7×10−5, Π′=4×10−15 and further to Π=1×10−12 after the multiple pathway correction that probability of pattern being produced by chance (Π) is 1×10−12.
Using algorithm 3b, the privileged pathway elements (μ) is 6 which are 4 ligands, 2 receptors. The consistent aberrant privileged genes (τ) is 3, which are 2 ligands, 1 receptor. The inconsistent aberrant privileged genes (τ′) is 0; non-privileged pathway elements (q) is 9; consistent aberrant non-privileged genes (n) is 7; and inconsistent aberrant non-privileged genes (n′) is 2.
Inputting these values gives a probability of pattern being produced by chance ranges depending on the value of β from 6×10−5 for β=1 to 1×10−12 for β=0.
Our results showed that while EGFR was upregulated, there did not appear to be any activity in the rest of its pathways, so it was concluded that this upregulation was not clinically significant—i.e., that regardless of its upregulated state, it did not appear to be an important driver of malignancy in this tumor. It was then learned that the patient had been previously treated by Tarceva in response to a (positive) test for mutations in EGFR, and had shown no response on this therapy. Subsequent administration of Avastin as part of “trial and error” did show a partial response—Avastin targets VEGF. Our results indicated VEGF as a target.
As there are 6 aberrant genes inconsistent with the VEGF pathway, algorithm 1b is applied, and it yields a probability of pattern being produced by chance (Π) is 2×10−10.
In this case, the total pathway elements (q) is 47, which are 2 ligands (VEGF A, B); 2 receptors (KDR, FLT-1, both in the VEGFR family); 12 PI3K members (Cα, Cβ, Cγ, Cδ, C2α, C2β, C2γ, C3, R1, R2, R3, R5); 2 PLCγ forms (PLCγ1, PLCγ2); 3 AKT forms (AKT1, AKT2, AKT3); 2 PKC forms (PKCα, PKCβ); 6 additional in survival branch (14-3-3σ, 14-3-3ε,FKHR, eNOS, BAD, Bcl XL, Bcl 2); 2 SOS forms (SOS 1, SOS2); 6 RAS forms (HRAS, KRAS, MRAS, NRAS, RRAS, RRAS2); 2 MEK forms (MEK1, MEK2); 5 ERK forms (MAPK1, MAPK3, MAPK6, MAPK7, MAPK12); and 3 additional in proliferative branch (SHC, GRB2, c-Raf).
The total aberrant genes consistent with hypothesis (n) is 20, which are 1 ligand (VEGF A); 1 receptor (KDR); 5 PI3K members (Cα, Cβ, C2α, C3, R1); 1 PLCγ form (PLCγ2); 3 AKT forms (AKT1, AKT2, AKT3); 1 PKC form (PKCα); 1 additional in survival branch (14-3-3ε); 1 SOS form (SOS1); 1 RAS form (KRAS); 1 MEK form (MEK1); 2 ERK forms (MAPK1, MAPK7); and 2 additional in proliferative branch (SHC, GRB2).
The total number of aberrant genes inconsistent with the hypothesis (n′) is 6 which are 3 additional in survival branch (14-3-3σ, FKHR, Bcl XL); 1 SOS form (SOS2); 1 RAS form (RRAS2); and 1 MEK form (MEK2).
The total number of possible pathways (N) is 283 and the cut-off probability (p) is 0.05. Then Π+=2×10−14, Π−=0.03 (these represent the “raw” probabilities of the hypothesis being true and the reverse hypothesis being true, respectively), leading to Π′=9×10−13 and further to Π=2×10−10 after the multiple pathway correction.
Inputting the specific p-values associated with the positive genes (ranging from 0.04 to 1×10−4) and those for the negative genes (ranging from 0.05 to 0.005) into algorithm 2b leads to: Π+=3×10−33, Π−=5×10−5, Π′=7×10−29 and further to Π=2×10−26 after the multiple pathway correction, giving a probability of pattern being produced by chance (Π) is 2×10−26.
Applying algorithm 3b, the privileged pathway elements (μ) is 4, which are 2 ligands+2 receptors.
The consistent aberrant privileged genes (τ) is 2; inconsistent aberrant privileged genes (τ′) is 0; non-privileged pathway elements (q) is 43; consistent aberrant non-privileged genes (n) is 18; and inconsistent aberrant privileged genes (n′) is 6.
This provides a probability of the pattern being produced by chance, depending on the parameter β, ranging from 8×10−6 for β=1 to 2×10−26 for β=0.
TABLE 3
Regulated Networks from Lung Cancer Sample
Focus
ID
Molecules in Network
Score
Molecules
Top Functions
1
ALCAM, ARL6IP5, ATP2A2, CANX, CAV1, CAV2,
25
35
Cancer, Cellular Assembly
CD59, CEP170, CLINT1, CLTA, DOM3Z, EMP2,
and Organization, Cellular
EPS8, FLOT1, FLOT2, GJA3, GNA11, HCCS,
Function and Maintenance
KPNB1, LIN7C, LRRFIP2, NGFRAP1, PCMT1,
RBM4, SH3BGRL2, SLC1A1, SQRDL (includes
EG: 58472), SVIL, TACSTD1, TCEB2, TENC1,
TMOD3, WFS1, WSB1, XPNPEP3
2
ARHGAP18, ARMC8, BCL2L1, BECN1, BFAR,
25
35
RNA Post-Transcriptional
CLCN3, CLK1, CSPG5, DYNC1I2, DYNLRB1,
Modification, Cellular
DYNLT3, FGD2, FIP1L1, GANAB, GOPC, GRID1,
Function and Maintenance,
HTRA2, LUC7L, MKLN1, MPHOSPH6, NKTR,
Embryonic Development
RNPS1, SFRS2, SFRS4, SFRS6, SFRS8, SFRS11,
SFRS2IP, SRPK1, SRPK2, STK17B, VDAC1, ZFP91,
ZFR, ZRSR2
3
BTG3, CDK2, CDK2AP1, CIRBP, CNBP, CNOT1,
25
35
Cancer, Cell Cycle,
CNOT2, CNOT3, CNOT6, CNOT7, CNOT8,
Skeletal and Muscular
CNOT6L, ERH, LATS1, LXN, MOBKL1B, MRPS14,
Disorders
PABPC1, PAIP1, PAN3, PINX1, PPM1A, PSMF1,
PSPH, RBM7, RBMX, RNF126, RPL23A, RPL35A,
RPS16, RPS28, RQCD1, STK38, TAGLN2, TBCA
4
ABCC4, ALG3, ASXL1, ASXL2, ASXL3, CDK10,
25
35
Drug Metabolism,
CKS2 (includes EG: 1164), CNN3, CSRP1, CSTF3,
Molecular Transport,
DDX19A, DYNC1LI2, EBP, EIF4A2, EIF4G3,
Nucleic Acid Metabolism
FAM134C, G0S2, GLTSCR2, HCN2, IDI1, IFRD1,
MIF4GD, PDCD4, PHF19, PINK1, PMS2L3, PTEN,
RNASEH1, RPL22, RPL39, RPS13 (includes
EG: 6207), SEL1L, SHFM1 (includes EG: 7979),
TRAM1, TRAPPC3
5
ATF7, C4ORF34, C6ORF203, CLNS1A, CSDE1,
25
35
RNA Post-Transcriptional
EBF1, FBXL11, FLJ20254, GEMIN7, GPRC5C,
Modification, Cellular
ITSN2, KCTD3, LSM1, LSM4, LSM5, LSM8,
Development, Connective
MAP2K1, NARS, OLA1, PCDH9, PLEKHA5,
Tissue Development and
PPAP2B, PRIC285, PRMT5, SNRPD2, SNRPD3,
Function
SNRPE, SNRPG, SPTLC2 (includes EG: 9517), ST7,
STRAP, TBL1XR1, THUMPD3, TRAF3IP3, ZNF706
6
BRD2, BRP44, C11ORF61, C20ORF24, CIDEB,
25
35
Cancer, Immunological
CRBN, DFFA, DMXL2, DPM1, EFTUD2, GCN1L1,
Disease, Lipid Metabolism
HEATR1, KIAA0406, KIAA1967, MED1, MLSTD2,
MRPL41, NUP54, NUP188, OPA1, PDXDC1,
PRKD3, PTP4A3, RAB3GAP1, SATB1, SSR1, SSR3,
STRN4, TMEM33, TMEM41B, TOMM22,
TOMM70A, UBE4A, USMG5, ZNF294
7
ADNP, ARID2, ARID1A, ARID1B, ATG7, ATG10,
25
35
Gene Expression, Cellular
ATG12, C19ORF2, C20ORF67, CCNK, CEP76,
Assembly and
COL14A1, DHFR, DIDO1, EPB41L5, HELZ,
Organization, Cellular
HTATSF1, KCNRG, KIAA1279, MLLT10, PAICS,
Compromise
POLR2A, SF1, SF3A1, SF3A2, SF3B1, SF3B3,
SMARCA2, SMARCB1, SMARCC1, SRGAP3,
SUPT6H, TCERG1, WWP2, ZNF638
8
AFF4, AHCTF1, CCAR1, CDC2L6, CHD9, CROP,
25
35
Gene Expression, Cell
DDX50, DDX52, DIMT1L, EIF4A1, ELL2, GCOM1,
Signaling, Cell Cycle
IKZF5, KIAA1310, KPNA4, MED8, MED13, MED16,
MED19, MED23, MED24, MED25, MED26,
MED13L, MED7 (includes EG: 9443), NOP5/NOP58,
POLR2J, POLR2K, PUM2, RBM39, RECQL,
TARDBP, WDTC1, ZCCHC10, ZNF281
9
AFTPH, ALOX5, AP1G1, AP1S2, AP1S3,
25
35
Hematological System
C11ORF31, C4ORF42, CFB, CPSF3L, CREG1,
Development and Function,
DALRD3, DERL1, ERLIN2, ETF1, FAM14A,
Tissue Morphology, Cancer
GNPNAT1, HBB (includes EG: 3043), HBG2, HBZ,
IGF2R, KITLG (includes EG: 4254), KLHL24,
MID1IP1, PGLS, PLSCR1, PLSCR3, RANBP2,
SECISBP2, SELT, SEPHS2, SPG7, TXNIP, VCP,
YIPF3, ZNF207
10
ANKRD36B, ASMTL, C11ORF58, C14ORF147,
25
35
Cellular Movement,
CCL11, CDIPT, DPY19L1, FABP5, FKBP3,
Hematological System
FLJ11184, GLT8D3, JARID1B, KCTD12, KIAA0152,
Development and Function,
KLF10, LIMCH1, MCC, MT1X, MYH10, NKX3-2,
Immune Response
NSMCE4A, OSM, PAX9, PPIA, PRDX3, PRDX6,
PSD3, PSIP1, PUS7, RIPK2, RPL38 (includes
EG: 6169), S100A8, SMC5, TDO2, TXNDC1
11
ADM, BAT2D1, BTN2A1, CALCRL, CCR1, CD209,
25
35
Cancer, Cell-To-Cell
CEL, CLIC2, COL12A1, CUGBP2, CYBB, EDNRA,
Signaling and Interaction,
GPR34, GTPBP1, HIGD1A, HOXA9, IRX3 (includes
Skeletal and Muscular
EG: 79191), ITGAM, LOC339047, LY86, MSI2,
Disorders
NAALAD2, NANOG, NAP1L4, NPIP, NSUN5C,
OAZ1, OSBPL8, OTP, PHACTR2, RAB31, RAMP2,
SIM1, SRP72, TNC
12
ABCD3, API5, ASAH1, BCAP29, C1ORF142,
25
35
Genetic Disorder, Lipid
COX4NB, DC2, FADS1, GMPPB, HMGB3, KCNH7,
Metabolism, Metabolic
MAGED1, MLF2, MTHFD1L, NOVA1, PGRMC1,
Disease
PJA2, PRC1, RAB23, RGS1, RPE, SFXN1, SFXN4,
SRPRB, TOR1AIP2, TRIM37, TSC22D1, TSC22D4,
TTC35, UBLCP1, UQCC, YY1, ZC3H7A,
ZMYND10, ZNF580
13
ATE1, Catalase, CBWD1, CMTM3, CNIH4, CSK,
23
34
Cell Morphology, Cellular
DUSP16, DUSP22, EFS, ILK, IMP3, ITGA9 (includes
Development, Amino Acid
EG: 3680), KLF11, LIMS1, LPXN, MAPK1, METAP1,
Metabolism
OTUD4, PARVA, PARVB, PHF21A, PTPN12,
PTPN18, PTPN22, RGS5, RSU1, SCLT1, SEMA3F,
SLC4A1, ST5, STYK1, TEX10, TGFB1I1, TPR,
ZHX2
14
AIFM2, ANLN, ASPM, C14ORF106, DR4/5, EGFL6,
23
34
Cancer, Cell Cycle,
FAM3C, GLIPR1, HMGN2, IKIP, JMJD1C,
Genetic Disorder
LETMD1, MBNL2, MORC3, MPV17L, MTDH,
PPP4R2, PQLC3, PRODH (includes EG: 5625),
RECQL4, RFFL, SCO2 (includes EG: 9997),
SLC19A2, SMA4, SMG1, SNRK, STEAP3, TMED7,
TMEM97, TP53, TPRKB, TULP4, UBL3, ZMAT3,
ZNF84
15
ATIC, ATYPICAL PROTEIN KINASE C, BHLHB2,
23
34
Cell Morphology, Nervous
BHLHB3, C17ORF42, CLN6, CMBL, COX5B, DVL3,
System Development and
ECT2, EME1, FFAR2, HNRPDL, IL29, IL10RB,
Function, Cancer
IL13RA1, IL27RA, IL9R, KIF3A, KIF3B, LMO4,
LMO1 (includes EG: 4004), MIRN21 (includes
EG: 406991), PARD3, PARD6G, PITPNB, PRKCI,
PTCRA, RAB2A, SOCS7, SPCS2, STAT3, STMN3,
TMF1, ZNF148
16
AKT1, COL4A2, FBLN1, HEYL, HIPK3, KLF15,
23
34
RNA Post-Transcriptional
LIMD1, MATR3, METTL1, MORC4, MTCP1,
Modification, Cell
MTMR4, MUT, NDRG2, NEXN, NID1, PKC
Signaling, Post-
ALPHA/BETA, PLAC8, PLXDC1, POP4, PPHLN1,
Translational Modification
PPL, PSG3, RICTOR, RPP14, RPP25, SKIL, SVEP1,
THAP5, TRIB2, TSC1, TSKU, TTF2, UBE4B,
ZNF107
17
ABI2, APBB1IP, ARF5, B4GALNT1, CCDC53,
23
34
Cardiovascular System
DGKA, DMN, ENAH, GART, GTF3A, GTF3C1,
Development and Function,
GTF3C2, HIF3A, HTATIP2, KIAA0368, NCOA5,
Embryonic Development,
NRP1, NRP2, OPTN, PCM1, PLXNA1, PLXND1,
Organismal Development
RAB11B, RAB11FIP3, RAB11FIP4, Sema3,
SEMA3B, SEMA3C, SEMA6D, SNAI2, STC1,
TBC1D8, TMSB10, VASH1, VEGFA
18
AMOTL2, CASC3, DCP2, DCP1A, DCP1B, DDX6,
23
34
RNA Damage and Repair,
DEK, EIF2C1, EIF2C2, EXOSC6, G3BP2, Hdac1/2,
Viral Function, Cell Cycle
HIST1H3A, ING4, ITGB1BP1, KRIT1, MAGOHB,
PDS5A, RAD21, RBM8A, RELA, SKIV2L2, SMC3,
SMC1A, SRRM1, SRRM2, STAG1, STAG2, SYCP3,
UPF2, UPF3A, UPF3B, WAPAL, XRN1, ZDHHC8
19
CHI3L1, COL16A1, COMMD1, COMMD2,
23
34
Gene Expression, Cellular
COMMD3, COMMD10, DAB2, ELF1, ELF2, ELF4,
Development,
ELK3, ETS, ETS2, FLT1, HIVEP2, IPO8, KARS,
Hematological System
KPNA1, KPNA3, LMO2, LSM12, NFKB1, NFKBIL2,
Development and Function
NNMT, NUP50, RIPK5, SLC25A6, SLC39A14,
SRP19, SRP54, TRIM3, TRIP4, TUBB6, UBA3,
UBE2K
20
AMACR, ARL15, C16ORF14, CCDC90B, DBI,
23
34
Genetic Disorder,
EIF1AD, FAM101B, FAM123A, FUNDC2 (includes
Metabolic Disease, Cellular
EG: 65991), LAMA4, NACA, PCTK1, PEX3, PEX5,
Assembly and Organization
PEX10, PEX13, PEX14, PEX19, PEX26, PEX11B,
PMP22, RBM17, RNF135, SAT1, SCP2, SERPINB9,
SLC25A17, Soat, SOAT1, STAT1, TFG, TNFRSF10C,
TNRC6A, UBR1, ZHX1
21
AZIN1, BDP1, BNC2, BRD8, C20ORF20, CDK4-
23
34
Small Molecule
Cyclin D2, EAF1, EID1, EP400, EPC1, EPC2,
Biochemistry, Cancer,
FLJ20309, GMPS, H1F0, ING3, INOC1, JARID1A,
Skeletal and Muscular
LACTB, LIN37, MAGEF1, MORF4L1, MORF4L2,
Disorders
MRFAP1, OAZ2, PHF12, PLSCR4, RB1, RBBP6
(includes EG: 5930), REEP5, RSL1D1, RUVBL2,
SRPR, THOC1, THOC2, TRIM27
22
Ahr-aryl hydrocarbon-Arnt-Esr1, ATN1, BICC1,
23
34
DNA Replication,
C9ORF86, COL15A1, CSNK1G2, CUEDC2,
Recombination, and
CXORF45, DAZ4, DECR1, DNTTIP2, DZIP1,
Repair, Cellular
EFEMP1, ESR1, FAM103A1, GFI1B, HAT1, HYPK,
Development, Cellular
KIAA0182, LCOR, LENG8, MYST3, NELL1, PNRC2
Growth and Proliferation
(includes EG: 55629), QKI (includes EG: 9444), RBM9,
RBPMS, RCHY1, RERE, RNF138, SLC39A8, SLIT1,
TM4SF1, TRIM16, TRIM22
23
ASCL2, BCLAF1, C16ORF53, CCDC123,
23
34
Cellular Assembly and
CDC42EP3, CEBPB, CPB2, DPY30, GMCL1,
Organization, Cellular
Histone-lysine N-methyltransferase, LEMD3, MAP4,
Compromise, Protein
MLL2, MLL3, NCOA6, NFKBIZ, NSD1, PRPF3,
Synthesis
PSPC1, RAB10, RBM14, SART3, SEPT2, SEPT6,
SEPT7, SEPT8, SEPT9, SEPT11, SETD7, TFDP2,
TMEM176A, TPT1 (includes EG: 7178), TUBB, UTX,
WHSC1L1
24
AKAP2, BTF3 (includes EG: 689), C9ORF80, CCL18,
23
34
Cancer, Genetic Disorder,
CREBL2, ECM2, EMR2, EXT1, EXT2, FBXO9,
Nucleic Acid Metabolism
FOXN3, HIG2, HYOU1, IRS1, KIAA0999, KLF12,
LYK5, MARK3, MARK4, MRPS10, MRPS15,
NOL11, NUAK1, PMPCB, PRKRA, PRPS1,
PRPSAP1, PRPSAP2, Ribose-phosphate
diphosphokinase, RPS29, SAAL1, SNF1LK2,
SPARCL1, STK11, TUBE1
25
ATXN3, CDC25B, CHD4, CLOCK, CREBBP, CRY1,
23
34
Gene Expression,
CUX1, ERG, ETV1, EYA3, FOXM1, H3F3A, H3F3B,
Reproductive System
HMGN1, MAP3K7IP3, MBD1, NAPSA, NCOA1,
Development and Function,
NCOA2, NCOA3, NKX2-1, NPAS2, Pias, PIAS1,
Cell Morphology
PIAS2, PIAS3, PLAGL1, RBCK1, SFTPB,
SMARCE1, TACC2, TAGLN, TBX19, TP53BP2,
TROVE2
26
ABCA1, ACTC1, ACTG1, CADPS2, CDC2L5,
23
34
Developmental Disorder,
CENPF, DGKZ, DMD, DSTN, DTNB, EGLN1,
Genetic Disorder, Skeletal
EIF4B, ELP2, GAK, IFN ALPHA RECEPTOR,
and Muscular Disorders
IL6ST, JAK2, MAPK8IP3, MEP1B, MTIF2, NCAPG
(includes EG: 64151), NPM1 (includes EG: 4869), OS-
9, OSMR, PDLIM5, PTPRK, RASGRP1, SBDS,
SCN4A, SNTB2, SOCS5, SYNE2, TMSB4Y,
UBASH3B, UTRN
27
ANKRD44, DDX56, DUB, HECW1, MARCH7,
23
34
Post-Translational
MED20, UCHL1, UCHL3, UCHL5, USP1, USP3,
Modification, Behavior,
USP4, USP6, USP7, USP10, USP12, USP14, USP15,
Cellular Function and
USP18, USP28, USP31, USP32, USP33, USP34,
Maintenance
USP37, USP42, USP45, USP46, USP47, USP48,
USP53, USP54, USP9X, USP9Y, ZNF423
28
ABHD2, AGR2, ARL4C, ARRDC3, ATP1A3,
23
34
Molecular Transport,
ATP1B3, ATP1B4, C19ORF12, CCNE2 (includes
Cancer, Cell Death
EG: 9134), CDH11, CLEC2B, CTSH, CUTC,
DYNC1H1, FGF14, FXYD2, GMNN, HNRPUL1,
HPGD, KCNJ2, LGALS3, MFGE8, MGC16121, Na-k-
atpase, NADSYN1, PBRM1, PDLIM3, PGM2L1,
PLEK2, PRICKLE1, REST, S100A2, SCHIP1,
SMARCA4, TBX2
29
ANAPC5, BAT1, DICER1, DNA-directed RNA
23
34
RNA Post-Transcriptional
polymerase, DNM2, EXOC4, HNRPLL, HNRPM,
Modification, Gene
HSPC152, MED31, MGC13098, MSH6, NCAPH2,
Expression, Organ
NOLC1, NONO, ORAI2 (includes EG: 80228), PNN,
Morphology
POLR1D, POLR2C, POLR3C, POLR3F, POLR3H,
PPIG, PRPF4B, PTBP1, RAVER2, RBM4B, RPL37,
SFRS3, SFRS18, SMC4, SSB, STC2, TMPO,
ZCCHC17
30
ARIH1, BAG4, C11ORF9, CHORDC1, CKAP4,
23
34
Post-Translational
CTNNA1, DNAJB11, DOK3, EIF4E2, FANCC, FHL2,
Modification, Protein
HEPH, HFE, HNRPK, HSP90AA1, HSPA8, HSPA1A,
Folding, Cancer
HSPA1B, IFI44L, JINK1/2, LRRC59, PHLDA1,
PSPN, RET, RNF19A, S100A11 (includes EG: 6282),
SFN, ST13, SYPL1, TBC1D9, TFRC, TXN, XPOT,
YWHAE, YWHAQ (includes EG: 10971)
31
ATXN1, C14ORF139, C1ORF65, C20ORF77,
23
34
Lipid Metabolism, Small
C6ORF199, CADPS, CALCOCO2, DBNDD2, DZIP3,
Molecule Biochemistry,
FAS, FBF1, FBXL18, FLJ10404, GABARAPL2,
Molecular Transport
GBAS, LRSAM1, LUC7L2, METT11D1, MGAT4B,
MTERFD2, NASP, NOL3, PEA15, PHPT1, PUM1,
R3HDM1, SFRS14, SGMS1, SSFA2, TBC1D5,
TNFR/Fas, TTC19, TUBGCP2, TUBGCP4, UBAP2L
32
ARL5B, C12ORF35, C16ORF57, CAMTA1,
23
34
Cancer, Cell Cycle, Tissue
CCDC59, CROT, CTSB, DEDD, EEF1A1, GFM1,
Morphology
GPNMB, HSPE1, KIAA1712, KIF1B, NUDT3,
PAPSS1, PAPSS2, PHYH, Protein-synthesizing
GTPase, PRSS1 (includes EG: 5644), RAB2B,
RANBP3, RNF167, RPLP1, RSRC1, S100A10,
SCN11A, SCN5A, SLC25A13, SMAD4, SOX30,
THRAP3, TMPRSS3, TRIM33, WARS
33
CAPZA1, CAPZB, CCBL2, CCNA2, CDS2,
23
34
Dermatological Diseases
CGGBP1, Cyclin A, EMX2OS (includes EG: 196047),
and Conditions, Genetic
FAM19A2, FAM49B, FLCN, FMR1, FXR1,
Disorder, Nervous System
HIST1H2BC, KLHL3 (includes EG: 26249), METTL9,
Development and Function
MLL5, MSRA, MTPN, NAALADL2, NFYB, NPTX2,
PITPNM2, RABIF, RIMBP2, SLC25A28, SLMAP,
ST8SIA1, TBC1D22A, TM7SF3, TMEM87A, TTC3,
VPS52, ZMPSTE24, ZMYM3
34
ADAMTS9, AIM2, B3GNT5, BEX1 (includes
21
33
Amino Acid Metabolism,
EG: 55859), CARD14, CBR3, CD14/TLR4/LY96,
Post-Translational
CHM, CXCL16, ECOP, FNTA, FNTB, HDGF,
Modification, Small
IRAK1BP1, LY96, MFHAS1, NFkB, NKIRAS1
Molecule Biochemistry
(includes EG: 28512), NOL14, PAK1IP1, PNKD,
PTP4A2, RAB7A, RABGGTB, RAP2A, RASSF4,
RNF19B, SLC11A2, STK10, SUMO4, TNFAIP8,
TNFSF18, TRIM69, UNC5CL, WTAP
35
Akt, AKTIP, ARMCX3, ARS2, ASAH2, BRD7,
21
33
Lipid Metabolism, Small
C11ORF79, C1ORF103, C1ORF174, CEACAM6
Molecule Biochemistry,
(includes EG: 4680), DEXI, EIF3J, FKHR, GOLM1,
Post-Translational
IMMT, KBTBD7, KIAA1377, MAL2, MCAM,
Modification
MRPL44, NIPSNAP3A, PCTK2, PHIP, PPT1, RER1,
RPL13A, SEC14L2, SLC40A1, SOCS4, TACC1,
TPD52, TPD52L1, TXNDC9, UXS1, ZFAND3
36
ADI1, ANP32A, ANP32B, APEX1, CDKN1A,
21
33
RNA Post-Transcriptional
DDX17, DLG5, DSE, ELAVL1, ERO1L, FBXO38,
Modification, DNA
FEN1, Foxo, HMGB2, HNRNPA2B1, KIAA0101,
Replication,
KIF20A, KLF7, KPNA6, NPAT, Oxidoreductase,
Recombination, and
SEPW1, SET, SETBP1, SFPQ, SFRS1, SFRS12,
Repair, Gene Expression
SLC30A5, SNRPA1, TOPORS, TTLL5, VEZT,
VGLL4, WHSC2, WWOX
37
AP1GBP1, APBB2, BICD2, BZW1, BZW2, DEGS1,
21
33
Lipid Metabolism, Small
EDEM1, EGFR, Egfr dimer, EML4, GAS5, KDELR1,
Molecule Biochemistry,
KDELR2, LRRFIP1, MAN2B1, Mannosidase Alpha,
Cancer
MTM1, MTMR2, MTMR12, NEK6, NEK7, NEK9,
RPS25, RUSC1, SBF2, SCAMP1, SCAMP2,
SERINC3, SGSM2, SH3BGRL, SLC16A1, SNX13,
SURF4, TNXB, VDAC3
38
ACTR1A, ANKHD1, ATP7B, BICD1, C14ORF166,
21
33
Cell Cycle, Embryonic
CDC42SE1, CEP63, CHML, CLASP2, CLIP1,
Development, Hair and
CRK/CRKL, DCTN2, DCTN4, DIAPH3, DISC1, DST,
Skin Development and
Dynein, ERN1 (includes EG: 2081), EXOC1, GLRX,
Function
MAPK8, MAPRE1, MDFIC, NDEL1, NIN,
PAFAH1B1, RAB6A, RAB6IP1, RABGAP1,
SH3BP5, SPAG9, SPTBN1, TAOK1, TEGT,
TRAF3IP1
39
APTX, BACE1, Beta Secretase, BTBD14B,
21
33
Cancer, Dermatological
CALCOCO1, CCND1, CDK2-Cyclin D1, CHMP4A,
Diseases and Conditions,
COL4A3BP, CUL3, GGA2, GPBP1L1, GTPBP4,
Gene Expression
H2AFY, MAT2B, PAPOLA, PLEKHF2, PRR13,
PTPN9, RB1CC1, RTN3, RTN4, SP2, SPEF1, SPG21,
SPOP, STXBP1, SYT17, TSPYL2, UBE2Z, VAPA,
XRCC4, ZEB2, ZFHX3, ZNF639
40
Ap1, AP3B1, AP3D1, AP3M1, AP3S1, ARF1, BET1,
21
33
Cellular Assembly and
BLVRA, CD58, EEA1, GOSR1, GOSR2, MARCH2,
Organization, Molecular
PACS1, SCARB2, SCFD1, SEC22A, SEC22B,
Transport, Protein
SEC23A, SEC23IP, SEC24A, SEC24B, SEC24C,
Trafficking
SNAP23, Snare, STX6, STX7, STX16, USO1,
VAMP3, VAMP4, VAMP7, VPS11, VPS41, VPS45
41
ASPN, COL11A2, COL1A2, COL3A1, DLGAP4,
21
33
Cellular Movement,
EPB41L1, FBLIM1, FERMT2, FN1, FYB, HTRA1,
Reproductive System
IGBP1, Integrin&alpha, Integrin&beta, ITGAE,
Development and Function,
ITGAV, ITGB5, ITGB6, LTBP1, LTBP2, LTBP3,
Cell-To-Cell Signaling and
MGP, MICAL2, MID1, MYO10, PPP6C, SAPS2,
Interaction
SEC23B, SEC24D, SPARC, ST6GAL1, TGFB2,
TGFB3, TGFBI, TSPAN13
42
BAT3, CD99 (includes EG: 4267), COL8A1, CPSF6,
21
33
Cardiovascular Disease,
E3 HECT, EFEMP2, EPDR1, ERP27, FAM127B,
Cardiovascular System
FMNL3, GIGYF2, HRASLS3, HUWE1, KLHDC5,
Development and Function,
KLHL12, NFKBIA, NOTCH2NL, PCDH17,
Hematological Disease
PRPF40A, RAD23A, RIC8A, RPN1, RSRC2, SNRPN,
STCH, STIM2, TNRC6B, UBA6, Ube3, UBE3A,
UBE3B, UBQLN1, UBQLN2, UBQLN4, ZCCHC8
43
ADAD1, ADAR, ADARB1, ADD3, Adenosine
21
33
Genetic Disorder,
deaminase, AKR7A2, BRAP, CCDC92 (includes
Hematological Disease,
EG: 80212), CCNL1, CELSR2, DCBLD2, FHL1,
Protein Trafficking
FXC1, GSS, KLF6, LMAN1, MCFD2, MT1E, NFAT5,
OMD, PDGF BB, PHF10, PLEKHA1, RPL13,
SLC6A6, SLC7A1, TIMM10, TIMM13, TIMM23,
TIMM44, TIMM17A (includes EG: 10440), TIMM8A,
TIMM8B, TRIOBP, ZFP36L1
44
DDIT4, DDX5, DNASE1L3, ELMOD2, Esr1-Esr1-
21
33
Molecular Transport,
estrogen-estrogen, FAM130A1, GLCCI1, H1FOO,
Cancer, Cell Death
HNRPD, HSD17B12, ILF3, JMJD6, KHSRP, MED14,
MTL5, NR3C1, PHLDA2, POGK, POLDIP2,
POLR2B, PRKDC, PTMS, RNF10, RNF14, RPL34,
SELENBP1, TADA2L, TIA1, TIAL1, TNFAIP1,
TPST2, TRAP/Media, UNC45A, WDR37, WDR40B
45
ACBD3, APPL2, AUP1, C1ORF124, C4ORF16,
21
33
Cellular Function and
CDC40, CHMP1A, DAZAP2 (includes EG: 9802),
Maintenance, Molecular
DCUN1D1, EPPK1, EPS15, GAD, GGA1, GRB2,
Transport, Protein
HGS, HRBL, LAPTM5, MIST, NISCH, PHKA2,
Trafficking
RAB22A, RNF11, SHIP, SIGLEC7, SKAP2,
SMURF2, STAM, STAM2, STAMBP, UNC5C, USP8,
USP6NL, VPS24, VPS37C, ZFYVE9
46
Adenosine-tetraphosphatase, AGRN, ANGPTL2,
21
33
Molecular Transport, Cell-
ARL8B, ATP5C1, ATP5D, ATP5F1, ATP5I, ATP5J,
To-Cell Signaling and
ATP5J2, ATP5L, ATP5O, ATP6V0A2, ATP6V0C,
Interaction, Cellular
ATP6V0D1, ATP6V0D2, ATP6V0E1, ATP6V1A,
Assembly and Organization
ATP6V1C1, ATP6V1E1, CARS, CHMP2B, CPD,
CREB1, ETV6, H+-transporting two-sector ATPase,
ITIH2, ITIH4, LASS2, OPA3, PNO1, SLC18A2,
SLC2A3, TCIRG1, ZNF337
47
CAPRIN1, CCDC50, DHX9, EIF3A, EIF3B, EIF3C,
21
33
Protein Synthesis,
EIF3E, EIF3H, Fcer1a-Fcer1g-Ms4a2, Fcgr2,
Carbohydrate Metabolism,
FCGR2C, FYN, G3BP1, HNRNPU, HSPH1, IARS2,
Small Molecule
LUM, MDH1, NANS, PARN, RILPL2, RNMT, RPL6,
Biochemistry
RPL7, RPL10, RPL14, RPS2, RPS6, RPS3A, RPS4X,
SFRS10, SON, STAU1, TUBB3, YTHDC1
48
ADH4, ADH5, ADH6, alcohol dehydrogenase,
21
33
Molecular Transport,
ALDH2, AQP1, DHRS2 (includes EG: 10202), EIF5A,
Protein Trafficking, Small
FBXO33, IPO7, IPO9, MT1G, NR2F1, NR2F2,
Molecule Biochemistry
NUP98, NUP153, NUTF2, PAIP2, PURA, PURB,
RAN, RANBP5, RPL5, RPL19, RPS7, SP3, SP4,
TEAD1, TNPO1, Vegf, XPO7, YBX1, ZBTB7A,
ZFPM2, ZNF197
49
AZI2, CCDC47, CMTM6, COMT, COX11, COX4I1,
21
33
Gene Expression,
COX4I2, COX5A, COX6A1, COX7A2, COX7B,
Behavior, Cell Signaling
COX7C, COX8A, Cytochrome c oxidase, DARS,
GABPB2, IARS (includes EG: 3376), Isoleucine-tRNA
ligase, JTV1, JUNB, KIAA0090, LARS, LITAF,
LMO3, MARS (includes EG: 4141), MFN1, MTX1,
NAP1L1, OXTR, RAB3GAP2, SCYE1, SGPL1,
TANK, TMBIM4, TMCO1
50
AHCYL1, BCL6, BCL2L11, BCOR, BNC1,
21
33
Cell Cycle, Cellular
C1ORF19, CD79B, Cpsf, CPSF2, CPSF3, EMCN,
Development,
FLJ12529, FOXP3, GALNAC4S-6ST, GP1BA, GSR,
Hematological System
IGHM, IGJ, IGL@, Igm, ITPR2, LAGE3, LYN,
Development and Function
NUDT21, OSGEP, PCF11, PIGR, PRDM1, SAMSN1,
SDC1, SUMO3, SYMPK, TNFSF13, TNFSF13B, TSN
51
ARF6, ARMET, C15ORF29, C6ORF211, CA12,
21
33
Cell-To-Cell Signaling and
CA13, Carbonic anhydrase, CNDP2, DAP3, DDOST
Interaction, Embryonic
(includes EG: 1650), DMXL1, Dolichyl-
Development, Gene
diphosphooligosaccharide-protein glycotransferase,
Expression
EPAS1, GBE1, GLS, GSPT1, HIF1A, MAGT1,
MAOA, NDRG1, PCBP3, PDK1, PKIB, PLOD2,
PTPRG, RAB20, RGS10, RORC, RPN2, RPS9,
RPS26, SAP18, SHMT2, STT3B, TPP2
52
ARMC7, BPTF, CDK4, CDK4/6, Ctbp, CTBP1,
21
33
Cell Cycle, Cellular
DDX3X, DNAJA2, HCFC1, HDAC2, HNRNPA1,
Assembly and
HNRPH1, IFIT3, IKZF1, IKZF2, KCTD13, KPNA2,
Organization, DNA
LPCAT1, MBD2, MLL, NBR1, OGT, PML, PTMA,
Replication,
RBBP4, RBBP7, RNF12, SAP30, SIN3A, SIN3B,
Recombination, and Repair
SMARCA1, UTP18, VTA1, ZC3HAV1, ZMYND19
53
14-3-3(&beta, &gamma, &theta, &eta, ζ),
21
33
Cancer, Genetic Disorder,
ARHGAP21, C16ORF80, C22ORF9, CAMSAP1L1,
Cell Cycle
CCNY, DOCK11, EHD1, EPB41L2, FRY, FRYL,
GAPVD1, HECTD1, ITGB3, KIAA1598, LAPTM4A,
LNX2, NUMB, OSBPL3, PHLDB2, PPFIBP1,
PRPF38B, RACGAP1, RASAL2, RASSF8, SAPS3,
SDHA, SDHAL1, SDHC, SMCR7L, Succinate
dehydrogenase, SYNPO2, TBC1D1, YWHAB,
YWHAG
54
C14ORF129, CD44, CDC42, CDK5RAP2, DDX58,
21
33
Immune Response,
DEF6, DKC1, DMP1, EEF1G, EPB41L3, FCGR2A,
Immune and Lymphatic
GDI1, GDI2, HNRPH3, IQGAP, IQGAP3, IQGAP1
System Development and
(includes EG: 8826), LOC196549, NOLA1, OAT,
Function, Cellular
PECAM1, PKN2, PRMT2, Pseudouridylate synthase,
Assembly and Organization
PTPRA, PTPRE, PTPRM, PTPRS, RAB4A, RAB5A,
RABEP2, RPUSD4, SMYD2, STARD9, TRIM25
55
AFG3L2, ATP11B, ATPase, CCNH, CCNT2, CHD1,
21
33
Gene Expression, Cell-To-
CRKRS, DHX15, EBAG9, FOXC1, GTF2H1,
Cell Signaling and
GTF2H2, HINT1 (includes EG: 3094), KIAA1128,
Interaction, Cellular
OFD1, PBX3, PRUNE, PSMC2, PSMC4, PSMD1,
Development
PSMD5, PSMD7, PSMD9, PSMD10, PSMD12,
PSMD13, RNA polymerase II, RNF40, RSF1, SAFB,
SETD2, SMYD3, SUB1, TCEA1, ZNF451
56
ABCC5, Actin, ARPP-19, BDH1, CAP1, ENC1,
21
33
Cellular Assembly and
FXYD3, GAS1, GLRX5, GPC5, GSN, HNRNPC, IPP,
Organization, Hair and
JUN/JUNB/JUND, KIAA1274, KRAS, LIMA1,
Skin Development and
MAPRE2, MLPH, MMD, MSLN, MTRR, PFDN4,
Function, Cellular Function
PHACTR1, RAB27A, RASGRF2, RBM41, SEC11C,
and Maintenance
SERBP1, SYTL4, TES, TPM2, TPM3, TPM4, VBP1
57
ABCC6, Alpha Actinin, ANXA1, ANXA2,
21
33
Cellular Assembly and
ARHGAP17, BLID, Calpain, CAPN2, CAPN3,
Organization, Cell
CAPN7, CAPN8, CASP9, CREBZF, DDX46, EZR,
Signaling, Skeletal and
KIAA0746, LAP3, MGC29506, MRPL42 (includes
Muscular System
EG: 28977), PCYT1A, PTPN1, RBM5, RPL17, RPL23,
Development and Function
RPL31, RPL27A, RPS21, RPS24 (includes EG: 6229),
SCYL3, SORBS2, STOM, TLN1, TTN, VAT1, VCL
58
14-3-3, APC, APCDD1, BUB3, CRIPT, CRKL,
21
33
Cancer, Cell Cycle,
DLG3, DR1, EHF, EMP1, GAB2, GAB1 (includes
Respiratory Disease
EG: 2549), HCFC1R1, HDLBP, KAL1 (includes
EG: 3730), LPHN1, MACF1, MDM4, MDM2 (includes
EG: 4193), MED28, MET, NF2, RAB40C, RAPGEF1,
SLC7A11, SPSB3, STAT5a/b, SULF1, TFEB, TFEC,
TMEM22, TP63, WT1, ZEB1, ZNF224
59
ARID5B, ASCC3, CHD2, CXCL10, DHX30,
21
33
Gene Expression, RNA
DYNLL1, FAM120C, FAM98A, GPRIN3, HDAC3,
Trafficking, Developmental
HMGB1 (includes EG: 3146), HNRPA3, MECP2,
Disorder
MTA1, MTR, NCOR2, NCoR/SMRT corepressor,
NUFIP2, PPP4R1, RARB, RBAK, RREB1, RSBN1,
SENP6, SERPINB1, SMC6, SUMO1, TBL1Y,
VitaminD3-VDR-RXR, WDR33, ZFP106, ZNF226,
ZNF362, ZNF711, ZNF354A
60
adenylate kinase, AK2, AK3, AK3L1, ALKBH6,
21
33
Cardiovascular Disease,
AQP3, ARL17P1, ATG2B, BMP2K (includes
Cell Morphology, Genetic
EG: 55589), BSDC1, C14ORF105, C16ORF61,
Disorder
C1ORF50, C2-C4b, CD55, CD302, COQ10B,
CTDSPL2, FAM107B, GLA, GPATCH2, GPR39,
HNF1A, HNMT, HSPC111, LSG1, MCCC1,
MRPS18B, NRD1, PCNP, PHF2, TSC22D2, UROD,
WDSOF1, ZBTB20
61
ADAMTSL4, AGK, BCL2L14, C2ORF28, CCDC6,
19
32
Connective Tissue
CREB5, DNPEP, ERK, GPER, KLB, LOXL1, LPP,
Disorders, Hematological
MAFB, MAFF, MAFK, MPZL1, musculoaponeurotic
Disease, Organismal Injury
fibrosarcoma oncogene, NFE2, NFE2L1, NFE2L3,
and Abnormalities
NRF1, NTN4, PALLD, PPP2R2D, PRRX1, RP6-
213H19.1, SH3GLB2, SPRED1, SPRED2, SPRY,
SPRY1, SPSB1, TESK1, UBA5, WWP1
62
BMI1, Cbp/p300, CBX4, COL5A2, ERBB2IP,
19
32
Skeletal and Muscular
HIST2H2AA3, HOXA2, HOXA3 (includes EG: 3200),
System Development and
HOXB8, HOXB6 (includes EG: 3216), HOXD4,
Function, Embryonic
MAPKAPK3, MEIS1, PBX1, PBX2, PCGF2, PDZD2,
Development, Tissue
PHC1, PHC2, PHC3, PKNOX1, PKP4, POU3F2,
Development
RABGEF1, RAPGEF2, Ras, RNF2, RPS6KC1, RYBP,
SHOC2, Sox, SOX4, SOX11, SOX12, UBP1
63
ARCN1, CNKSR2, COPA, COPB1, COPB2, COPG,
19
32
Cellular Assembly and
COPG2, COPZ1, COPZ2, DOCK8, ENSA, Erm,
Organization, Cellular
GALNT1, GYS2, Insulin, KIAA1881, LRRC7, MCF2,
Compromise, Nervous
MOBKL2B, MSN, NME7, NT5C2, PELO, peptide-
System Development and
Tap1-Tap2, PTGFRN, RDX, RHOC, RHOJ, RHOQ,
Function
SLC25A11, SLC25A12, SPN, TAPBP, TCHP, TMED9
64
ACO1, AMPD3, AOAH, C1q, C1QBP, C1QC, C1S,
19
32
Cellular Function and
C3-Cfb, CD93, CFH, CFI, Complement component 1,
Maintenance, Small
CP, CR1, EGR1, FAM83C, FTH1, FTL, IGKC,
Molecule Biochemistry,
IREB2, KIAA0754, KRT10, KRT6A, LECT1, MLX,
Gene Expression
MLXIP, MNT, MXD1, NAB1, NOPE, PAPPA,
PNMA2, SAFB2, TNS3, ZNF292
65
ALAD, ARL5A, CBX1, CBX3, CBX5, DIAPH2,
19
32
Gene Expression, DNA
EEF1B2, eIF, EIF1, EIF5, EIF1AX, EIF2S3, EIF5B,
Replication,
EZH1, HELLS, IFITM2, INSR, IPO13, JAK1/2, LBR,
Recombination, and
METAP2 (includes EG: 10988), MKI67, MKI67IP,
Repair, Protein Synthesis
OGN, OLFM2, RNF13, RWDD4A, SEZ6L2, SFRS5,
SP100, SURF6, Tk, TK2, ULK2, ZFAND5
66
3-hydroxyacyl-CoA dehydrogenase, ACAT1, Acetyl-
19
32
Amino Acid Metabolism,
CoA C-acetyltransferase, CALU, CSE1L, CSTB,
Small Molecule
DNAJB1, F8A1, FAM120A, FDFT1, GCLC, GCLM,
Biochemistry, Drug
HADHA, HADHB, HSD17B4, HSP90AB1, HSPA5,
Metabolism
IBSP, IFITM1, LDL, LMO7, NPC2, OSBP, PDCD6,
PON1, PON2, RCN1, S100A9, SCARF1, SCARF2,
SFXN3, SPTLC1, TMEM43, TRIP12, XPO1
67
BAG5, CCT2, CCT4, CCT5, CCT8, CCT6A,
19
32
Post-Translational
CDC37L1, Chuk-Ikbkb-Ikbkg, CORO1C, FKBP4,
Modification, Protein
FKBP51-TEBP-GR-HSP90-HSP70, HIST1H2BK,
Folding, Drug Metabolism
HIST4H4 (includes EG: 121504), HSBP1, Hsp70,
HSPA4, HSPA6, HSPA9, JMJD2A, MAN2B2,
MAP3K1, MAP3K3, PACRG, PDCL, PHF20L1,
PTGES3, RPAP3, RPL3, RPL10A (includes EG: 4736),
RPL37A, RPS11, RPS23, RPS27L, TCP1, WDR68
68
BGN, CTGF, DCN, ELA2, ELN, ERBB3, ERBB4
19
32
Cellular Movement,
ligand, FBN1, FCN2, Fibrin, GLB1, Igfbp, IGFBP4,
Cancer, Cell-To-Cell
IGFBP5, IGFBP7, LGMN, MMP2, MMP10, MMP17,
Signaling and Interaction
MMP25, NPEPPS, PAPPA2, PCSK6, RECK, S100A4,
SERPINA1, SERPINA3, SPOCK1, THBS1, THBS2,
TIMP2, TMEFF2, TNFAIP6, VCAN, ZBTB33
69
AKT2, Alcohol group acceptor phosphotransferase,
19
32
Amino Acid Metabolism,
BRAF, CCR7, CDC42BPA, CDK6, CFL1, CK1,
Post-Translational
Cofilin, CSNK1A1, CSNK1G1, CSNK1G3, DAPK1,
Modification, Small
DDX24, DPYD, DYRK1A, EIF2AK2, EIF4E,
Molecule Biochemistry
FAM98B, FOXO1, GRK6, HIPK1, LIMK2, MIB1,
PA2G4, PDS5B, PRKRIP1, PRKX, RAE1, SGK1,
SNX5, SSH2, TMEM9B, TPI1, TXNL4B
70
APLN, COL11A1, Cyclin B, CYP2R1, DDX42,
19
32
Nutritional Disease, Cell
DNM3, DNM1L, Dynamin, EWSR1, HIPK2, HMGA1,
Cycle, Hair and Skin
HSPB8, MPHOSPH8, NFYA, NRGN, p70 S6k,
Development and Function
PACSIN2, PDPK1, PFN1, RANBP9, RPS6KA3
(includes EG: 6197), RPS6KB1, SLC9A1, SUPT7L,
TAF1, TAF2, TAF4, TAF8, TAF10, TAF11, TAF13,
THRA, TP53INP1, VDR, XPO6
71
ATXN2L, C5ORF22, CASP2, Caspase, CENPO,
19
32
Cellular Function and
DGCR8, DHPS, E2f, EIF2AK3, EIF4G2 (includes
Maintenance, Connective
EG: 1982), FAM33A, GAS2L1, GOLT1B, Hdac,
Tissue Development and
HDAC4, HDAC9, HIST1H2AB, HOPX, LRDD,
Function, Viral Function
MOCS2, MTCH1, NUSAP1, PSME3, RNASEN,
ROCK1, RPL9 (includes EG: 6133), SNRPC, SPEN,
STK24, SUMO2 (includes EG: 6613), TMEM126B,
WBP4, XAF1, ZBTB1, ZNF160
72
ARRB1, CD164, CPNE8, DDX27, DNAH3, DNAL1,
19
32
Genetic Disorder,
GLRX2, NADH dehydrogenase, NADH2
Neurological Disease, Cell-
dehydrogenase, NADH2 dehydrogenase (ubiquinone),
To-Cell Signaling and
NDUFA3, NDUFA4, NDUFA5, NDUFA6,
Interaction
NDUFA11, NDUFA12, NDUFAB1, NDUFB1,
NDUFB2, NDUFB4, NDUFB5, NDUFB7, NDUFB8,
NDUFB10, NDUFC2, NDUFS1, NDUFS5, NDUFS8,
NDUFV1, NDUFV3 (includes EG: 4731), RPL7L1,
RPS17 (includes EG: 6218), RTF1, SCYL2, ZRANB2
73
ANK1, ARID4B, ATF7IP2, B4GALT1, B4GALT5,
19
33
Gene Expression, RNA
B4GALT6, B4GALT7, CCNL2, CDC2L2, CTSD,
Post-Transcriptional
CYP7B1, Esr1-Estrogen-Sp1, FUSIP1,
Modification, Carbohydrate
Galactosyltransferase beta 1,4, IFITM3, MGEA5, MI-
Metabolism
ER1, MRC1, MUC5B, NFYC, PDHX, POGZ, RHAG,
SFRS7, SFTPA2B, SLC11A1, SP1, SUDS3, TFAM,
TFB2M, TRA2A, TXNDC12, WDFY3, ZFYVE20,
ZNF587
74
ADAMTS5, AIF1, COP1, CYSLTR1, ERAP1,
18
31
Cell Signaling,
FAM105B, GALNS, GNL1, IL-1R, IL-1R/TLR, IL1B,
Carbohydrate Metabolism,
IL1R1, 1L1R2, IRAK, IRAK1, IRAK2, IRAK3,
Nucleic Acid Metabolism
IRAK4, IRAK1/4, MYLK3, NUCB2, PELI1,
PLXDC2, PTP4A1, SCUBE2, SEPP1, TICAM2, TIFA,
TIRAP, TLR4, TLR8, TLR10, UAP1, UGDH,
ZC3H12A
75
ABCC9, AIM1, AJAP1, APOD, ARL4A, BCL9, Bcl9-
18
31
Gene Expression,
Cbp/p300-Ctnnb1-Lef/Tcf, CDH9, CELSR1, CST4,
Embryonic Development,
CTNNB1, DKK3, GPR137B, Groucho, HES1, ID4,
Tissue Development
KCNIP4, L-lactate dehydrogenase, LDHA, LDHB,
LEF1, LEF/TCF, LEO1, MGAT5, MUC6, NLK, PLS3,
PTCH1, SFRP2, TAX1BP3, TCF4, TCF7L2, TLE1,
TLE4, UEVLD
76
AHR, Ahr-aryl hydrocarbon-Arnt, C12ORF31,
18
31
Gene Expression, Hepatic
CAMK2N1, COL1A1, COL5A1, COL5A3, CSF1,
System Disease,
Esr1-Estrogen, GPR68, GUCY1B3, HOOK3, IFNGR1,
Dermatological Diseases
KLF3, LOX, MRC2, MSR1, NFIA, NFIB, NFIC,
and Conditions
NFIX, Nuclear factor 1, P4HA1, RAB5B, RIN2, RIN3,
RRAS2, SERPINH1, SKI, SLC25A5, SLC30A9,
SLC35A2, SWI-SNF, TPD52L3, TRAM2
77
C10ORF119, DNA-directed DNA polymerase,
18
31
DNA Replication,
EIF2AK4, H2AFX, HUS1, IL1, LNPEP, MCM4,
Recombination, and
MCM10, Mre11, MRE11A, ORC2L, ORC3L, ORC6L,
Repair, Cell Cycle, Gene
PAPD5, POLE4, POLH, POLS, RAD1, RAD17,
Expression
REV1, RFC3, RPA, RPA3, TAX1BP1, TERF1,
TERF2IP, TNKS, TNKS2, TNKS1BP1, TP53BP1,
XAB2, XPA, XRCC5, ZBTB43
78
ACTR1B (includes EG: 10120), ACVR1, ACVR1B,
18
31
Embryonic Development,
ACVR2A, ACVRL1, CUL5, FBXO3, FBXO24,
Tissue Development,
FBXO34, FGD6, FOXH1, INHBC, NARG1, NAT5,
Organismal Development
PLEK, PREB, SAMD8, SAR1A, SMAD2, Smad2/3,
SMURF1, SNX1, SNX2, SNX4, SNX6, TBL2, Tgf
beta, TGFBR, TGFBR1, TRIM35, Type I Receptor,
UHMK1, VPS29, VPS35, ZFYVE16
79
Ahr-Aip-Hsp90-Ptges3-Src, ANKRD12, ATRX,
18
31
Cell Morphology,
CUGBP1, CXCL12, DNAJ, DNAJC, DNAJC1,
Hematological System
DNAJC3, DNAJC7, DNAJC8, DNAJC10, DNAJC11,
Development and Function,
DNAJC14, DNAJC15, EIF2AK1, FEZ2, FUBP1,
Immune and Lymphatic
Hsp90, ICK, IFNAR2, IRF6, LATS2, LOXL2,
System Development and
MARCKS, MBNL1, NEK1, NLRP3, PPP5C, PRKRIR,
Function
SAV1, STK3, STK4, TXNL1, ZNF350
80
ANKH, BTBD1, CBFB, CHST8, CK1/2, CLPP,
18
31
Post-Translational
COL4A1, CSF1R, CSNK2A1, CSNK2B, CTNNBL1,
Modification, Cancer, Gene
CXCL6, GALNT2, GALNT3, GALNT4, GALNT7,
Expression
Glutathione peroxidase, GPX3, KYNU, LOC493869,
LSAMP, LTA4H, MUC1, PCSK7, peptidase,
Polypeptide N-acetylgalactosaminyltransferase,
POU2F2, PRNP, PRNPIP, RUNX1, SEPHS1, SPP1,
TDP1, TOP1, TRIM5
81
AAK1, Adaptor protein 2, AMD1, AP2A1, Arf, ARF3,
17
31
Cellular Function and
ARFGEF1, ARFIP1, DLEU2, EHBP1, EHD2, Fcgr3,
Maintenance, Cell
FCGR3A, GABAR-A, GABRB1, GABRP, GBF1,
Signaling, Molecular
HIP1, LAMP1, LDLRAP1, PABPN1, PICALM, RIT1,
Transport
RPL4, RPL11, RPL15, RPL21, RPL24, RPL27,
RPL28, RPL12 (includes EG: 6136), RPLP2, SHC1,
SYT6, SYT7
82
ANAPC1, ANTXR, ANTXR1, ANTXR2, BIRC2,
17
31
Post-Translational
BIRC6, CBL, E3 RING, PARK2, Proteasome, RNF5,
Modification, Protein
SORBS1, SUGT1, TCEB1, UBE2, UBE2A, UBE2B,
Degradation, Protein
UBE2D1, UBE2D2, UBE2D3, UBE2E1, UBE2E2,
Synthesis
UBE2F, UBE2G1, UBE2G2, UBE2H, UBE2I,
UBE2J1, UBE2N, UBE2R2, UBE2V1, UBOX5,
UBR3, XIAP, ZMYM2
83
3alpha-hydroxysteroid dehydrogenase (A-specific),
16
30
Drug Metabolism, Genetic
AKR1C1, AKR1C2, AKR1C3, CLSTN2, COL6A1,
Disorder, Lipid Metabolism
COL6A3, DPYSL2, DPYSL3, DPYSL4, DPYSL5,
DYRK2, FAT, GOLGA3, GRIP1, IGF1, KIF5B,
KLC1, KLC2, KLF9, ME1, MGA (includes
EG: 23269), NCOA4, OSBPL7, SERCA, SNED1,
SRA1, SRD5A1, SRR, STRBP, T3-TR-RXR, Thyroid
hormone receptor, TRAK1, TRAK2, Trans-1,2-
dihydrobenzene-1,2-diol dehydrogenase
84
ARF4, ARL6IP1, BRCC3, BTRC, CAND1, Cyclin D,
16
30
Cellular Development,
Cyclin E, E3 co-factor, FBXW7, FBXW11, FZR1,
Hematological System
ITCH, JUN, MEF2B (includes EG: 4207), MIA3,
Development and Function,
N4BP1, NEDD9, PXDN, Scf, SEC63, SEC61A2,
Immune Response
SEC61B, SEC61G, SERP1, SKP1, SKP2, SLC30A1,
SNAPC5, SUMF2, TAL1, TCF12, TCF20, Tcf 1/3/4,
TLOC1, WEE1
85
ALOX5AP, B2M, CD4, CD74, CD1C, CD1D, CIITA,
16
30
Immune Response, Cell-
CLEC7A, CSF2, Csf2ra-Csf2rb, CTSS, FCER1G,
To-Cell Signaling and
HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1,
Interaction, Immunological
HLA-DQA1, HLA-DQB1, HLA-DQB2, HLA-DRA,
Disease
HLA-DRB1, HLA-DRB4, Ifn alpha, IL12B, IL5RA,
LAMP2, LYPLA3, MHC Class II, MHC II-&beta,
Mhc2 Alpha, MOG, RBM3, RFX5, SLC39A6, SSBP1
86
ARHGAP1, ARHGAP5, ARHGAP6, ARHGAP8,
16
30
Cell Signaling, Cell-To-
ARHGAP9, ARHGAP15, ARHGAP29, ARHGEF2,
Cell Signaling and
BNIP2, CADM1, CASK, CD226, CNKSR3, DLC1,
Interaction, Tissue
EVI5, F11R, GRLF1, INPP5A, Itgam-Itgb2, JAM,
Development
JAM2, JAM3, KIFAP3, LIN7B, MAGI, MAGI1,
MLLT4, NRXN2, PVR, RAC1, Ras homolog, RhoGap,
SH3BP1, SYNPO, THY1
87
BRD4, CAM, CLDN1, CLDN10, CLDN11, CLDN15,
16
30
Cellular Movement,
DDX18, Guk, INADL (includes EG: 10207), ITGB1,
Nervous System
LAMA2, LAMB1, LAMB3, LAMC1, MPDZ, MPP2,
Development and Function,
MPP5, Nectin, NPNT, OCLN, PLEKHA2, Pmca,
Gene Expression
PPP1R9A, PPP1R9B, RAB21, TBX5, TEAD2,
TEAD3, TEAD4, TGOLN2 (includes EG: 10618),
TSPAN, TSPAN3, TSPAN6, WWTR1, YAP1
88
ASH1L, AURKA, CCDC71, CENTD2, CHFR,
16
30
Cell Morphology, Cellular
CLDND1, ERCC6, GSTA4, GTF2A1, HIST2H3D,
Assembly and
Histone h3, JMJD2C, KIAA0265, LARP2 (includes
Organization, Cell Cycle
EG: 55132), LOC26010, MAGED2, MTUS1, NAIP,
NFATC2IP, NUP37, NUP43, PARP10, SAR1B,
SEC13, SEC31A, SETMAR, Taf, TFIIA, TFIIE,
TFIIH, TM4SF18, UBB, WDR1, ZBTB44, ZDHHC11
89
ANGPT2, ATP2B4, C7ORF16, Calcineurin protein(s),
16
30
Skeletal and Muscular
CBLB, CHP, CKM, CTSL2, DBN1, DLG1, FBXO32,
System Development and
GPI, GRIN2C, MAPK9, MARCKSL1, MEF2,
Function, Tissue
MEF2A, MEF2C, MYOZ2, Nfat, NFAT complex,
Morphology,
NFATC2, NIPBL, ODF2L, PARP14, PDDC1, Pp2b,
Dermatological Diseases
PPP3CA, PPP3CB, PPP3CC, PRSS23, RCAN1, SOD1,
and Conditions
TRAPPC4, XPNPEP1
90
AFF1, ALP, ANAPC13, ARNT, BMP, BMP7,
16
30
Cellular Development,
BMP8A, C10ORF118, CDC16, CDC27, CEP135,
Connective Tissue
CFDP1, CTDSP2, CXXC5, EXPH5, KIAA0256,
Development and Function,
KIAA0372, KIAA1267, MAST4, PAX8, PRDM4, Rar,
Cell Signaling
RNF123, Smad, SMAD1, SMAD3, SMAD5, SMAD9,
Smad1/5/8, TMEM57, ZDHHC4, ZMIZ1, ZNF83,
ZNF251, ZNF557
91
ABCC1, ACTA2 (includes EG: 59), CAMLG, CCND2,
16
30
Tissue Morphology,
CDKN2C, CDKN2D, CES2 (includes EG: 8824), Dgk,
Cancer, Reproductive
ERP29, ESD, ETS1, Fgf, FGF13, FKBP5, Glutathione
System Disease
transferase, GSTM2, GSTM1 (includes EG: 2944),
GSTO1, IFI16, INHBA, ITGB2, MGST1, MGST2,
MTHFD2, NFE2L2, PCYT1B, PMF1, PPIB, PXR
ligand-PXR-Retinoic acid-RXR&alpha, Rb, RUNX2,
TCF3, THRB, TYMS, ZNF302
92
ADAM9, ADAM10, ADAM12, ADAM17, ADAM21,
16
32
Developmental Disorder,
ADAM23, ADAM30, ADK, APBA2, APLP2, APP,
Neurological Disease,
BLZF1, C5ORF13, CHN1 (includes EG: 1123),
Organ Morphology
ERBB4, GNRHR, GORASP2, ITM2B,
Metalloprotease, NCSTN, NECAB3, Notch,
PCDHGC3, PPME1, PROCR, PSEN1, Secretase
gamma, SH3D19, SLC1A2, SPON1, SPPL2A,
TM2D1, TMED2, TMED10, YME1L1
93
ANAPC10, CCNG2, EEF2, FGFR1OP, GLUL,
15
29
Cancer, Cell Death,
Glycogen synthase, HSPD1 (includes EG: 3329), L-type
Reproductive System
Calcium Channel, M-RIP, MAP2K1/2, Mlcp, MPST,
Disease
MYCL1, PARK7, PP1, PP1/PP2A, PPP1CB, PPP1CC,
PPP1R8, PPP1R10, PPP1R11, PPP1R12A, PPP2CA,
PPP2R1B, PPP2R2A, PPP2R2B, PPP2R5C, PRDX1,
PTPN7, RAB18, RAB11A, RAB11FIP1, RALA,
RAP1A, WDR44
94
AKAP, AKAP1, AKAP4, AKAP7, AKAP8, AKAP9,
15
29
Protein Synthesis,
AKAP13, AKAP10 (includes EG: 11216), BMPR,
Molecular Transport,
C3ORF15, CBFA2T3, CD40, FLJ10357, GLO1,
Protein Trafficking
LZTS1, MYCBP, PDZK1, Pka, Pki, PRKACB,
PRKAR1A, PRKAR2A, PSMC3IP, RAB13, Rap,
RFX2, RPL30, SEP15, SLC30A7, sPla2, SUSD2,
TYRP1, UGCGL1, XCL1, ZNF652
95
AGTPBP1, ANK2, ANK3, BCL10, CASP7, CD3,
15
29
Cell Death, Hematological
CD3E, CFLAR, Ciap, CTLA4, DIABLO, ERC1,
Disease, Immunological
FASLG, FOXP1, IKBKB, IKK, ITPR, MALT1,
Disease
MOAP1, NF-κ B, PDPN, PPFIA1, PPM1B,
PTPN2, PTPRC, ROD1, SOS2, TCR, TNFAIP3,
TNFRSF10A (includes EG: 8797), TNFRSF10B,
TNFSF10, TNIP2, TRAF3IP2, TXNRD1
96
ANKRD17, BCL3, C1ORF41, C2ORF30, CCDC82,
15
29
Inflammatory Disease,
EFR3A, ERMP1, FILIP1L, FOXO3, HBP1, Hd-
Renal Inflammation, Renal
perinuclear inclusions, HDAC8, HERC3, Hsp27, Ikb,
and Urological Disease
KLF5, MON2, MRCL3, MYO6, Nos, NSFL1C,
PHF17, RARA, RBL2, SFRS15, SMG5, SMG7,
SNX3, SPG20, Tnf receptor, TNFRSF1A, TXLNA,
Ubiquitin, UFC1, VHL
97
AK5, BMI1, BNIP3 (includes EG: 664), BTBD11,
15
29
Tissue Development, Cell
C10ORF58, CAV1, CBX4, COG5, DBT, DDEF1,
Cycle, Cellular
FLJ38973, HIST2H2AA3, JARID1C, LOC653441,
Development
MCRS1, MEG3 (includes EG: 55384), NFX1,
NUCKS1, PCGF2, PHC1, PHC2, PRKD1, PXN,
RING1, RNF2, RPL7, RPL22, RYBP, TERT,
TMEM70, VEGFA, XRCC5, YWHAQ (includes
EG: 10971), ZNF313, ZRANB1
98
ADC, ANXA4, CD244, CKS2 (includes EG: 1164),
14
28
Cancer, Gastrointestinal
CTSB, CTSL2, EDNRA, ELK3, FARS2, FBN1, FOS,
Disease, Tumor
GCNT1, GNL2, heparin, HNRNPA2B1, IL15, IL1R1,
Morphology
KIAA1600, MGAT4A, MSLN, NUBP1, Ornithine
decarboxylase, P2RY1, PCNX, PUNC, RPS18,
SERPINE2, TCEA1, TFPI, THBS4, TNC, TXNRD1,
VWA1, ZNF33A
99
ANGEL2, AP2A1, ARL1, ARMC1, BRF2, C7ORF58,
14
25
RNA Post-Transcriptional
C9ORF64, CCDC59, CHP, COPB2, DPM1, HNF4A,
Modification, Cellular
KIAA1704, MRPL3, MRPL32, MRPS21 (includes
Assembly and
EG: 54460), PDXDC2, SF3B1, SLC44A1, SLMO2,
Organization, Carbohydrate
SRPRB, SSR3, TBC1D20, TIMM23, TMEM33,
Metabolism
TNPO3, TOE1, TXNL4B, ZNF644
100
ABI1, ACTR2, ACTR3, AHNAK, Alpha actin,
14
28
Cellular Assembly and
ARHGDIA, Arp2/3, ARPC2, ARPC3, ARPC4,
Organization, Cell
ARPC5, ARPC1B, ARPC5L (includes EG: 81873),
Signaling, Cell
C3ORF10, C8ORF4, CACNB2, CORO1B, CTTN, F
Morphology
Actin, FER1L3, G-Actin, HCLS1, IQGAP2, IQUB,
MAST2, NCF4, NCKAP1, PCDH24, PFN, Pkc(s),
SSH1, Talin, WASF1, WASF2, WIPF1
Biopsied tumor tissue from the patient was assayed for gene expression using Agilent transcription mRNA profiling and compared to the normal expression profile obtained from a database. 7,826 Genes had expression ratio thresholds of 3-fold up- or down-regulation, and a significance P-value of 0.05.
Using a tool provided by Ingenuity Systems, the of 7,826 genes were subjected to an algorithm which finds highly interconnected networks of interacting genes (and their corresponding proteins). Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways which could provide potential therapeutic targets or, if possible, clusters of interacting proteins which potentially could be targeted in combination for therapeutic benefit.
An initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. The networks that were assembled by the protein interaction algorithm for up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:
Cellular Growth and Proliferation
Hematological System Development
and Function
Immune Response
Endocrine System Disorders
Immunological Disease
Metabolic Disease
Viral Function
Carbohydrate Metabolism
Molecular Transport
Connective Tissue Disorders
Organismal Injury and
Abnormalities
This overall pattern is consistent with what one might expect from the global gene expression of an endothelial cell-derived tumor as compared to normal blood vessel, which helps to confirm that the signals are from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is in the table at the end of this example.
The list of over- or under-expressed genes was scored for associated negative or adverse cellular functions. The highest scoring category was liver proliferation. (
Most notably, a large, highly interacting network of regulated genes was found in the tumor sample centered on the angiotensin II receptor, type 1 (AGTR1; AT1R) including pre-angiotensinogen, the gene product precursor for angiotensin II, as shown in
The angiotensin pathway (or renin angiotensin system (RAS)) has a well established role in mediating blood pressure and volume, both systemically, and more recently in local organ systems. Indeed, the pathway is targeted therapeutically in the treatment of hypertension both via reduction of angiotensin II (ACE inhibitors) and by blocking AT1R receptors. More recently, however, ATIR has been implicated as a potential therapeutic target in a number of cancers, both through antimitotic and anti-vascularization mechanisms (Ino, K., et al., British J. Cancer (2006) 94:552-560.e; Kosugi, M., et al., Clin. Cancer Res. (2006) 2888-2893; Suganuma, T., et al., Clin. Cancer Res. (2005) 11:2686-2694), however, clinical and epidemiological results have been mixed (Deshayes, F., et al., Trends Endocrinol Metab. (2005) 16:293-299). It does represent an attractive target however, since there are numerous available ATR1 blockers such as the sartans, which have been widely prescribed (chronically) for hypertension.
Additionally, it is known from the literature that AT1R trans-activates the EGF receptor (EGFR) (Ushio-Fukai, M., et al., Arterioscler Thromb Vasc Biol (2001) 21:489-495) which is a demonstrated player in oncogenic processes and an established target for several cancer drugs (e.g., Erbitux™, Iressa®, Tarceva®). In the tumor sample, both EGFR and its family member and interacting receptor EGFR2 (Her2/Neu; erbb2) which is the target for the anti-cancer drug Herceptin®, are also significantly up-regulated. It should be noted, that another analyst found that EGFR protein, as demonstrated by immunohistochemistry (IHC) is not seen in the tumor sample. However, there is a body of literature demonstrating that the presence of the EGFR protein target, as demonstrated by IHC, is actually not a good predictor of clinical response to anti-cancer drugs that target EGFR (Chung, K. Y., et al., J. Clin. Oncol. (2005) 23:1803-1810.i). EGFR copy number change (which would be reflected in increased mRNA as detected by expression profiling) is a better predictor (Ciardiello, F., et al., N. Engl. J. Med. (2008) 358:1160-1174). A further validation study was performed using copy number which found that EGFR was indeed mutated.
One caveat of this analysis is the possibility of “contamination” of tumor tissue with normal liver tissue, which could potentially be a confounding variable. In fact, both AT1R and EGFR are expressed in higher amounts in normal liver than in normal blood vessel, which at least in principal could explain the over-expression of these two targets. One finding, however, makes this possibility less likely: While the exact mechanism for the trans-activation of EGFR by ATR1 activation is unknown, there is evidence that a key intermediate is the gene NOX1 (Ding, G., et al., Am. J. Physiol. Renal Physiol. (2007) 293:1889-1897). NOX1 was highly up-regulated in the tumor sample (
There is a second issue which makes the angiotensin pathway a potentially attractive target to emerge from this analysis. While the internal validation described above indicates that the tissue samples used for the gene expression study are indeed primarily tumor tissue, any profiling based on macro-dissection of tissue always has the possibility of measuring some signal from tumor stroma as opposed tumor cells per se. The angiotensin pathway, however, has been implicated in tumor biology both via a mitotic effect and a tumor vascularization effect. Angiotensin receptors localized to the stroma are thought to play a role in tumor vascularization, while tumor receptors are thought to mediate a mitotic effect. Therefore there might be a relevant to therapeutic role of decreasing activity in this pathway regardless of whether the overexpression is happening in tumor or the stroma.
The overall gene expression findings are consistent with an endothelial-derived tumor in the liver, based on global gene regulation. In addition, one particular pathway emerged from the analysis which has several potential points of therapeutic intervention that would not have been considered as part of the standard oncology approaches. In particular, the precursor angiotensin II (AGT), its receptor (AT1R; AGTR1) are both up-regulated, as well as EGFR and EGFR2 (Her2/Neu; erbb2). AGTR1 transactivates EGFR, which in turn heterodimerizes with EGFR2; the activated receptor is known to play a role in oncogenesis. Therefore, targeting AT1R, EGFR or EGFR2, possibly in combination, is suggested. Furthermore, the angiotensin pathway may represent a particularly robust target with respect to localization, as there may be benefit to blocking both tumor or stroma activity.
For the VEGF pathway, algorithm 1 gives probability of pattern being produced by chance (Π) as 1×10−7.
The total pathway elements (q)=25, which are 1 ligand (VEGF), 1 receptor (VEGFR), 16 downstream in survival branch (PI3K, PLCV, PIP2, PIP3, DAG, IP3, CA2, 14-3-3σ, XHR, AKT, eNOS, PKC α/β, BAD, BcI XL, NO, BcI 2), 7 in proliferative branch (SHC, GRB2, SOS, Ras, c-Raf, MEK 1/2, ERK 1/2). The total aberrant genes consistent with hypothesis (n)=12; which are 1 ligand (VEGF), 1 receptor (VEGFR), 7 downstream in survival branch (PI3K, PLCV, 14-3-3σ, XHR, AKT, PKC α/β, BcI XL), 3 in proliferative branch (SHC, GRB2, ERK 1/2). The total number of possible pathways (N): Assume ˜200 based on XXX canonical pathways within Ingenuity, with a cut-off probability (p)=0.05.
Tumor samples (melanoma metastases to lung) and normal tissue samples from the same patient (surrounding lung tissue) were obtained at biopsy. Affymetrix transcription profiling data (Hu 133 2.0 Plus), consisting of tumor vs. control gene expression ratios were generated using mRNA from this tissue. These data were filtered to obtain genes with an expression ratio threshold of 1.8-fold up- or down-regulation, and a significance P-value of 0.05.
In addition, DNA samples were processed using Affymetrix SNP Array 6.0 to determine genomic segments of amplification or deletion, referred to herein as Copy Number/Loss of Heterozygosity (CN/LOH) analysis. Individual genes contained in the amplified or deleted segments were determined using a genome browser.
A total of 5,165 genes from transcription profiling were passed on to network analysis. The filtered list of 5,165 genes was subjected to an algorithm (Ingenuity Systems) that finds highly interconnected networks of interacting genes (and their corresponding proteins). Proprietary software tools were used to integrate the networks with the CN/LOH data. Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways, which could provide potential therapeutic targets or, if possible, clusters of interacting proteins that potentially could be targeted in combination for therapeutic benefit.
In addition to the dynamic networks created on the fly from the filtered genes, expression and CN/LOH data can be superimposed onto static canonical networks curated from the literature. This is a second type of network analysis that often yields useful pathway findings.
Before looking for potential therapeutic targets, an initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. This serves as a crude measure of quality control for tissue handling and microarray processing methodology. The networks that were assembled by the protein interaction algorithm from the filtered list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The three top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:
Molecular Transport
Cellular Development
Lipid Metabolism
Cancer
Reproductive System Development and
Gene Expression
Function
Cell Signaling
RNA Post-Transcriptional
Modification
This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue. These are very high-level general categories and by themselves do not point towards a therapeutic class. However they help to confirm that we are looking at signals from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is set forth in Table 4.
In addition to a network analysis of the filtered list of 5165 regulated genes, the entire filtered list of genes was scored for associated cellular functions. The highest scoring category was cancer. (
Three major findings emerged from the analysis. They are presented below in order of the judged strengths of the emergent hypotheses. It should be noted that while the first hypothesis is scientifically stronger, the key drugs that target the pathway are still in clinical trials. An already approved drug may more easily target the pathways in the second and third hypotheses.
First Hypothesis
Cyclin-dependent kinase 2 (CDK2) was found to be highly up-regulated (19-fold) in the tumor. CDK2 is necessary for cell cycle progression from G1 to S phase (
In this patient's tumor sample, there is an additional reason to suspect that CDK2 inhibition may be effective: CDK2 is normally deactivated by interaction with protein kinase C-eta (PKCeta; PRKCH; Kashiwagi, et al., 2000). The CN/LOH analysis of the tumor indicates that in the tumor sample PKCeta is deleted, suggesting that CDK2 may be permanently in its more active form. Thus, CDK2 is both transcriptionally up-regulated, and post-transcriptionally, is devoid of the deactivating influence of PKCeta. Thus, these data provide information that indicates the presence of CDK2 activity, a parameter not measured directly.
CDK2 activation leads to hyperproliferation via its phosphorylation of, and subsequent de-activation of retinoblastoma protein (Rb), a tumor suppressor. Active, de-phosphorylated Rb binds to the transcription factor E2F and prevents activation by E2F of genes necessary for cell cycle progression. Thus, phosphorylation of Rb by CDK2 prevents this cell cycle arrest by Rb. Although the mRNA levels of Rb and E2F are not up-regulated in the tumor sample, their activity is a function more of their phosphorylation state than transcription level. The up-regulation and chronic activation of CDK2 would produce a higher phosphorylation state and hence lower activity of Rb, and greater activity of E2F in promoting cell proliferation. As described above, in preclinical studies, melanoma cells are particularly vulnerable to CDK2 inhibition compared with normal tissues and other cancer cells (Tetsu and McCormick, 2003), which makes CDK2 a particularly attractive target. There are several CDK2 inhibitors in development, notably flavopiridol and CYC202, with trials ongoing for melanoma and other cancers.
Table 5 summarizes the evidence from the integrated expression and CN/LOH studies bearing on the hypothesis of CDK2 pathway dysregulation in the tumor. CDK2 was highly up-regulated and constitutes the strongest evidence. PRKCH is deleted, which also strongly supports the idea of CDK2 hyperactivity. We did not see up-regulation of Rb or E2F, however, we would not expect to, as these two pathway members are regulated by CDK2 at the level of protein functional activity, not transcriptionally. Thus, their expression levels are considered neutral, with respect to the CDK2 hypothesis. Indeed, we outline further studies below which could bear on the status of these proteins, and could potentially support or refute this hypothesis.
Second Hypothesis
V-src sarcoma viral oncogene homolog (SRC) a tyrosine kinase, is over-expressed in the tumor sample. SRC is a tyrosine kinase that is involved in several signaling pathways related to oncogenic processes and has been implicated in several cancers including melanoma. It plays a central role in modulating the ERK/MAPK pathway as shown below (
SRC is a molecular target of the drug dasatanib, which is a dual BCR-ABL kinase and Src family kinase inhibitor. It is currently approved for imatanib-resistant CML and treatment-resistant Ph+ ALL. It is also in clinical trials for metastatic melanoma.
Third Hypothesis
LCK, a member of the Src tyrosine kinase family, is normally expressed in T-lymphocytes and is a target of leukemia drugs. However, it has also been investigated as a melanoma target, and recently the inhibitor dasatanib (approved for use in CML and ALL) has been shown to induce cell cycle arrest and apoptosis, and inhibit migration and invasion of melanoma cells (Eustace, et al., 2008). The central role of LCK in the SAP/JNK pathway and the expression and deletion status of several other pathway members, indicating dysregulation of this pathway, is shown in
For the SAP/JNK pathway, algorithm 1a gives probability of pattern being produced by chance (Π)=0.02.
The total pathway elements (q)=11, which are LCK, 1 upstream (TCR), 2 intermediaries before JNK (MEKK2, MKK4/7), JNK, 6 downstream of JNK (p53, AFT-2, Elk-1, c-Jun, NFAT4, NFATc1). The total aberrant genes consistent with hypothesis (n)=5, which are LCK, 1 upstream (TCR), 1 intermediaries before JNK (MEKK2), JNK, 1 downstream of JNK (c-Jun). The total number of possible pathways (N): Assume ˜200 based on XXX canonical pathways within Ingenuity and the cut-off probability (p)=0.05.
Concurrent inhibition of LCK and SRC might be expected to enhanced efficacy given the over-expression of both these targets in the tumor tissue, and their involvement in different but complementary pathways involved in oncogenesis and tumor maintenance. It should be noted, however, that because of its role in T-cell activation, inhibition of LCK could possibly have an immunosuppressant effect.
Our current technology collection is relatively insensitive to the types of measures that would evaluate immunotherapy as a potential recommendation. While we address biological questions about the tumor itself, more information either about systemic immune function, or specific populations of tumor-associated T-cells would be necessary to address this option. We are currently investigating the feasibility of adding this capability at a later date.
Additional Insight
We did however note a finding which, in hindsight, may be consistent with the patient's successful response to immunotherapy. In the tumor sample, the gene for Complement factor H (CFH) is deleted. CFH is a protein that regulates complement activation and restricts complement-mediated cytotoxicity to microbial infections. There is literature demonstrating that down-regulation of CFH in cancer cells sensitizes them to complement attack which can inhibit their growth in vitro and in vivo (Ajona, et al., 2007). To the extent that the patient's response to anti-CTLA4 therapy is related to a complement-mediated component of the immune response, the deletion of CFH might be a predictor of this vulnerability of the tumor.
Summary
Several dysregulated pathways with possible connections to melanoma were found: Cyclin-dependent kinase 2 (CDK2; inhibited by several drugs currently in development, including flavopiridol and CYC202), v-src sarcoma viral oncogene homolog (SRC), and lymphocyte-specific protein tyrosine kinase (LCK; both inhibited by the approved drug dasatanib). These targets all play roles in cancer-related signaling pathways, and have been specifically linked in the literature to melanoma progression. Additionally, deletion of the CFH gene, which can sensitize cells to complement attack, could possibly help explain the vulnerability of the tumor to immunotherapy.
Two drugs, flavopiridol and CYC202, targeting CDK2 are currently in clinical trials for melanoma and other cancers. Dasatanib, which targets both SRC and LCK, is approved for several leukemias, and is also in clinical trials for melanoma. Thus, while the first hypothesis (CDK2 pathway) is scientifically stronger, the key drugs that target the pathway are still in clinical trials. The pathways in the second two hypotheses (SRC and LCK) may be more easily targeted by an already approved drug.
The following steps are taken further to validate these hypotheses:
Elucidation of CDK2 pathway dysregulation at the protein level to determine the activation state of CDK2 and Rb as assessed by phosphorylation status. Thus, immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to CDK2 (Thr160) and Rb (Thr821), is performed as hypophosphorylation of CDK2 and hyperphosphorylation of Rb would support the validation of CDK2 as a target.
Elucidation of SRC pathway dysregulation at the protein level to determine if downstream effectors of SRC are hyper-activated. Thus, immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to ERK1/2, downstream effectors of SRC with respect to cell growth and proliferation, is performed as hyperphosphorylation of ERK1/2 indicates over-activation by SRC and supports the validation of SRC as a target.
Elucidation of LCK pathway dysregulation at the protein level to determine if downstream effectors of LCK are hyper-activated. Thus, immunohistochemistry of tumor sections and control tissue is performed using phospho-specific antibodies to JNK1/2/3, downstream effectors of LCK with respect to cell growth and proliferation, as hyperphosphorylation of JNK1/2/3 indicates over-activation by LCK and supports the validation of LCK as a target.
In vitro validation of the CDK2, SRC, and LCK hypotheses is performed using a cell line derived from tumor. Tumor cells from a fresh sample of tumor tissue are cultured and maintained in vitro, followed by treatment with CDK2 inhibitors, or dasatanib (SRC/LCK inhibitor), to assess anti-proliferative effects of these agents. Measured endpoints are cell proliferation (using, e.g., ATP charge) and apoptosis (using one of several readily available assays).
In vivo validation of the CDK2, SRC, and LCK hypotheses is performed using xenograft models, such as a mouse xenograft model derived from cultured tumor cells (described above). This model is used to test CDK2 inhibitors and dasatanib for anti-tumor effects, using change in tumor size as the measured endpoint.
These are the complete networks of interacting genes generated from the filtered list of up- and down-regulated genes. The Score column represents the overall level of interconnectedness within each network, and the Top Functions column describes cellular functions that are over-represented (relative to chance) in each network, as determined by the individual annotation of each gene in the network.
TABLE 4
Regulated Networks from Melanoma Tumor Sample
Focus
ID
Molecules in Network
Score
Molecules
Top Functions
1
ADPRH, C11ORF67, C19ORF42, C1ORF163, C4ORF19, C6ORF123,
32
35
Molecular Transport,
C6ORF208, CCDC115, CRYZL1, DDX47, HBS1L,
Cellular Development,
HMGN4, HNF4A, INTS4, JTB, KIAA0409, MLF1IP, MRPL22,
Lipid Metabolism
MRPL53, MRPS35, MRPS18C, PPP2R3C, SLC26A1,
SLC7A6OS, SNX11, SPATA6, STARD7, TAPBPL, TBC1D16,
TIGD6, TMEM79, TMEM63A, TRIM4, TXNDC14,
ZSCAN18
2
ADNP, ASNS, BRD3, C19ORF12, CALCRL, CDH11, CDH16,
32
35
Reproductive System
CSH1, DYNC1H1, FADS3, FGF10, FMO2, GPR6, GPR56,
Development and
GTPBP4, HOXA7, HPGD, LMO7, LOXL2, MAP7, MSI2,
Function, Gene
NADSYN1, NOTCH2NL, PSG4, PSG9, PTPN12, RBM33, S100A13,
Expression, Cancer
SMARCA4, ST6GALNAC4, TBX5, TEX10, TMEM123,
WWTR1, ZNF143
3
ANAPC1, BAT1, C3ORF26, CCNC, DCD, DDX52, DICER1,
32
35
Gene Expression, Cell
DIMT1L, EXOC4, HDAC6, HNRNPM, HNRPLL, LMNA,
Signaling, RNA Post-
LSM11, MED6, MED12, MED13, MED16, MED17, MED22,
Transcriptional
MED25, MED31, MYBBP1A, NOC4L, NONO, PPIG, PTBP1,
Modification
RAB25, RBM39, RPS15A, SFRS3, SMC4, SNRPD1, TOR1AIP1,
UBTF
4
C10ORF54, C11ORF58, CCND2, CIAPIN1, CREM, DAD1,
32
35
Gene Expression,
DDT, DHRS1, EIF3M, ETFB, EWSR1, FAM49B, FBXL18,
Cancer, Cell Cycle
GLRX3, HDAC3, HPRT1, IKBKE, KIAA1683, MRPS18B,
MTPN, NSUN4, PDIA6, PLSCR1, PPIB, PTRH2, RAB37, RHOB,
RPL22, SPCS2, SPG7, TMEM111, TMSB4Y, TNFRSF14,
ZNF638, ZNF764
5
ALOX15B, CRKRS, DDX17, ELOVL1, FIP1L1, FMN1, FNBP4,
29
34
RNA Post-
FUS, HIST1H1C, HNRNPA1, HNRNPA2B1, KRT31,
Transcriptional
MYBPH, MYEF2, NES, P38 MAPK, PLEKHB2, PRPF40A,
Modification, Lipid
RNPS1, SF1, SFPQ, SFRS1, SFRS2, SFRS4, SFRS6, SFRS9,
Metabolism, Small
SFRS10, SFRS2IP, SNRPA1, SRPK1, TOP1, TWF2, U2AF1,
Molecule Biochemistry
ZFHX3, ZFR
6
ABI2, ADAM22, ANXA11, BCOR, BPTF, C17ORF59, CBLC,
29
34
Cellular Assembly and
CCKAR, CEP290, CHMP7, CHMP4B, CHST12, CPNE4,
Organization, Cellular
HNRNPH3, Mapk, MAZ, NDE1, OLIG2, PCGF1, PCM1, PCNT,
Development, Cellular
PDCD6, PLSCR3, PTPN23, PTPRH, SH3KBP1, SMARCA1,
Growth and Proliferation
SNX13, SRI, TPM2, TRIB1, TUBGCP2, UGCGL1, UGCGL2, WISP1
7
APPBP2, C21ORF113, CRABP1, CUL4B, DDIT4, DECR1,
29
34
Gene Expression,
DLGAP1, DSCR3, ESR1, GRWD1, HMGN1, IQWD1, JAG2,
Cellular Assembly and
LDLRAP1, LRP2, MFAP5, NR2C1, NR2C2, NR2C2AP,
Organization, Lipid
NRIP2, PRDM2, Rbp, RBP2, RBP7, RORB, SMU1, SORBS2,
Metabolism
SYNJ2, SYNJ2BP, TAF1B, TCF7, UMODL1, WDR26, WDR61,
WWC1
8
BCCIP, C1ORF94, CCNA2, CCNB1, CCNB2, CCNF, CDC2,
29
34
Cell Cycle, Cancer,
CDC25A, CDCA3, CDK2, CDKN3, CHML, Cyclin B, DNM1L,
Reproductive System
FOXM1, GOLGA2, GORASP1, GORASP2, KLK4, MAP4,
Disease
MXI1, PAPOLA, PSENEN, PTMA, RAB1A, RNF17, SKI,
TMED2, TMED3, TMED10, UBE2A, UBR3, USO1, WEE1,
ZNF593
9
ACVR1B, ALAD, CCT2, CCT4, CCT7, CCT8, DKC1, DLX1,
29
34
Carbohydrate
EEF1G, FEN1, HARS, INHBC, INSR, KLF9, LZTS1, NARG1,
Metabolism, Genetic
NAT5, NOLA1, Pseudouridylate synthase, RAD1, RAD51AP1
Disorder, Hematological
RAD9B, RPUSD2, SEPP1, SEZ6L2, SFRS5, SIX4, SMURF1,
Disease
SNX1, SSR1, TRUB1, TXNDC9, ULK2, VPS35, ZFAND5
10
AKNA, ASF1A, ATRX, C19ORF50, CBX5, CHAF1A, CYBB,
29
34
Cell Cycle, Cellular
DNMT3A, EDN3, EXOC5, EXOC7, GIGYF2, Hdac, HOXA10,
Assembly and
ITGB3, KCNQ1OT1, LAMA4, MBD2, MOBKL1B,
Organization, DNA
MYO10, MYT1, NCF1, NR4A3, NSL1, PAX7, RBBP4, SNRPC,
Replication,
SNRPN, SRP9, SUZ12, TCEB3B, TLK1, TLK2, TRIM52,
Recombination, and
ZC4H2
Repair
11
APIP, ATG7, C1QBP, C1QTNF5, COIL, DCTD, DHX16, FAM115A,
29
34
Hematological System
FAM176A, GIPC2, GSPT1, HERC3, IMPDH2, INO80C,
Development and
KPNA3, LNX1, MAP1LC3A, MGEA5, NUMBL, PAICS,
Function,
PIAS4, PREPL, Proteasome, PTN, PTPRZ1, RNF112,
Hematopoiesis,
SAT1, SDC3, SHMT2, SLC25A36, TAC3, TAF1D, TNFRS
Organismal
F10D, WFDC2, ZNF277
Development
12
AKR1C1, COPS6, COPS8, COPS7A, COPS7B, CUGBP1, CUL4A,
27
33
Protein Synthesis, Gene
DIS3L2, DPYSL3, DYNLRB1, eIF, EIF5, EIF1AX, eIF2B,
Expression, RNA
EIF2B1, EIF2S1, EIF2S3, EIF3A, EIF3E, EIF3G, EIF3K,
Trafficking
EIF4A2, EIF4B, EIF4G3, EIF5B, HAPLN1, IGF1, IMPACT,
MBNL1, MPHOSPH6, NCBP2, PARN, RPS2, SGK3, VPRBP
13
ACE2, Angiotensin II receptor type 1, ANGPT2, APLN, APLNR,
27
33
Cardiovascular Disease,
CBFA2T2, CCL14, CRYBB3, GART, HEXIM1, HIF3A,
Genetic Disorder,
HSD17B14, HTATIP2, ICA1, Integrin alpha 2 beta 1, KLHL20,
Cardiovascular System
MELK, MKKS, NUP133, PTGER3, PTGS1, RASGRP3,
Development and
RPIA, SNRK, STARD8, STC1, STK16, SYNM, TBC1D8,
Function
TMSB10, TNPO2, VASH1, VEGFA, WDYHV1, ZNF124
14
ACP5, CCDC67, Ctbp, CYP17, DDX3X, DEFA4, FAM120C,
27
33
Endocrine System
HNRNPU, IFIT3, IKZF1, IKZF4, LPCAT1, LRCH4, MC2R,
Disorders, Genetic
MRAP, NCOR2, NR6A1, NUFIP2, NUP50, OGDH, OGT,
Disorder, Infectious
PCSK1N, PEX5L, PML, POMC, RBAK, RREB1, SART1, SEH1L,
Disease
SNW1, TRAK1, TSR1, ZC3HAV1, ZNF462, ZNF687
15
ACE, ADRA2B, AGTR1, AGTR2, ANTXR2, BCLAF1, CLNS1A,
27
33
Cardiovascular System
DRD2, FREQ, Gi-coupled
Development and
receptor, GRM8, HMGB1 (includes EG: 3146), HNRNPA3,
Function, Cardiac
HTR1D, IPO9, KCNAB1, KCND2, KCNE4, KCNIP2, KCNIP4,
Arrhythmia,
KCNJ1, KCNQ1, LRP4, MTUS1, NKTR, OPRM1, Pka, PPP1R9A,
Cardiovascular Disease
PPP1R9B, RPL19, RPS7, RPS19, SNPH, TNP2, WWP2
16
14-3-3(β, γ, θ, η, ζ), AHI1, ASCL2,
27
33
Cell Morphology,
BUB1B, CADM1, CALM3, CEP152, CRTAM, CTPS,
Cellular Compromise,
CTSD, EMP3, EPB41L3, FRMD6, HECTD1, Hsp90, ICK, LARP1,
Molecular Transport
LYST, MLXIP, NLRP1, NLRP3, P2RX7, PIH1D1, PLEKHF1,
PPFIBP1, PRMT5, RPAP3, SLC4A7, SMYD2, SNCG,
SSH1, SSH2, WDR77, YWHAB, YWHAG
17
ANPEP, ATP1B1, ATP1B3, BASP1, BCAM, BMP1, CCDC80,
27
33
Organismal
CDH17, CDX2, CHRD, COL5A2, CTSLL3, Cyclin A, Cyclin
Development, Cell
E, DMP1, FAH, FOXC1, GZMK, HOXC10, KIAA1274,
Morphology, Skeletal
KRAS, LAMA2, LAMA5, LAMC2, LGALS3, MYBL2, NRN1,
and Muscular System
PBX2, RAB20, TBX1, TFDP1, TLL2, TPM3, VAPA, YLPM1
Development and
Function
18
ABCC1, ACHE, ACLY, ARNTL, BHLHB3, CLMN, DAO, ELA2,
27
33
Cellular Compromise,
ENPP2, FOSL2, FOXA2, FXR1, GABPA, GCK, GMFB,
Lipid Metabolism,
GPAM, GPR146, HECW2, Hexokinase, HN1, IER3, MYOD1,
Molecular Transport
NAP1L3, NOV, ONECUT2, PROC, SDC1, Sod, SOD1, SOD2,
STAB2, TAT, THBS1, TMC1, TP73
19
Adaptor protein 1, AP1G1, AP1GBP1, AP1S1, AP1S2, ARCN1,
25
32
Cellular Assembly and
ARMC6, BICD1, COP I, COPB1, COPB2, COPE, COPG2,
Organization, Cell
CPE, CXCL14, DNASE1, GGA1, GGA3, GUSB, Hdac1/2,
Morphology, Cell-To-
IL8, ING1, KIF13A, LAT2, LPAR3, M6PR, PACS2, PIGR, POLDIP2,
Cell Signaling and
PTGER1, SAP30, SCAMP1, STK36, TFF3, VANGL1
Interaction
20
ADARB1, AKR7A2, BAT2, CCNO, CDC2L2, Cpsf, CPSF1,
25
32
RNA Post-
CPSF2, CTH, DCBLD2, ELOVL2, GREM1, GTF2I, HDAC10,
Transcriptional
HMG CoA synthase, HMGCS1, HSD3B7, IARS, MAGEA6,
Modification, Cell
NUDT21, OMD, PDGF BB, PSMF1, QARS, RPL13, SBDS,
Death, Embryonic
SFRS7, SNTG1, SPTBN5, SRPRB, SSR3, SYMPK, SYNE1,
Development
TRIOBP, VAPB
21
AMH, CHM, CXCL2, ERC1, GDF9, LDL, MYO5A, NPC2,
25
32
Cellular Assembly and
OSBPL6, PON2, PPFIA4, PRPS1, PTP4A2, Rab5, RAB22A,
Organization, Hair and
RAB27A, RAB27B, RAB4A, RAB6A, RABAC1, RABGGTB,
Skin Development and
Ribose-phosphate diphosphokinase, RPH3A, SNCB, SPTG21,
Function, Organ
ST14, STAR, SYP, SYTL2, SYTL5, TNFAIP6, TRIM2,
Morphology
TRIM3, UBE2K, ZFYVE20
22
ABCB6, ANKHD1, APOD, ARAP1, ASXL1, BAZ2A, C5ORF34,
25
32
Embryonic
CBX2, CRK/CRKL, DOCK1, DOT1L, DYRK3, EHMT1,
Development, Tissue
ELP4, GRINL1A, Histone h3, Histone-lysine N-methyltransferase,
Development, Gene
HOXC5, JMJD6, MLL3, PARP10, PCGF2, PHC2,
Expression
PHC3, RNF2, RPS9, RYBP, SCMH1, SRRM2, SYNE2, TFCP2,
TM4SF18, TMEM70, XIST, ZDHHC11
23
ATM, Basc, BCL11A, CDYL, COL19A1, COL9A2, COL9A3,
25
32
DNA Replication,
DUSP26, EHF, EIF2AK3, FBXL11, GINS2, HDAC2, HDAC4,
Recombination, and
HDAC9, HLA-DRA, HMGB3, HSF4, Hsp70, LIG4, MDC1,
Repair, Cellular
MED23, Mre11, MRE11A, NBN, PDS5A, POT1, SMC3,
Development,
SMC1A, TERF1, TFDP2, TP53BP1, WAPAL, XRCC5,
Hematological System
YY1
Development and
Function
24
ADCY10, Ant, ATF7IP, CBR1, CDT1, CKMT1B, DBN1, DTL,
25
32
Cellular Assembly and
ERCC8, GTF2H2, GTF2H3, GUCA1A, GUCY, hCG, HTRA2,
Organization, Nucleic
KIF5B, NIPBL, NPR1, NPR2, ODF2L, OPA1, PNOC,
Acid Metabolism, Small
PPID, PRSS23, S100B, SLC25A4, SLC25A6, SLC25A13, SLC4A4,
Molecule Biochemistry
SVEP1, TMEM158, TRAPPC4, TTC3, VDAC1, ZNF518A
25
AURKA, AURKB, BTRC, CAPRIN1, CELSR2, CKAP5, CSTF3,
25
33
Cancer, Renal and
DTYMK, E3 co-factor, FZR1, G3BP1, ID4, IDI1, KLF6,
Urological Disease,
KPNA1, KRT4, LRRFIP1, PFKL, PFKM, PINK1, PTEN, PTPRN2,
Nucleic Acid
RAB10, RNASEH1, RRM1, RRM2, Scf, SEL1L, SERPINH1,
Metabolism
SKP2, SMOX, SORD, TACC1, TCF12, TNFAIP1
26
ANLN, ARL6IP1, ASPM, ATAD2, ATM/ATR, BLZF1, BRCC3,
24
34
Cancer, Cell Cycle,
C14ORF106, CDC14A, CKAP2, CPOX, CTSF, EEF1E1,
Gastrointestinal Disease
FIGNL1, FXYD3, HUWE1, MAGEA2, NUP85, PIGF, PIGG,
PPA1, PPP4R2, PRKRIR, RCHY1, SLC19A2, SLC6A6,
SNAPC5, TCN2, TEAD2, TFAM, TMEM97, TNFRSF10C,
TP53, TULP4, UBA6
27
ADAP1, Adaptor protein 2, AGXT, AHNAK, APOBEC3C,
24
32
Neurological Disease,
CACNB2, CACYBP, CNBP, GABAR-A, GABRA5, GABRB3,
Developmental Disorder,
GABRE, GABRG1, GABRG3, GABRP, GABRQ, GABRR1,
Psychological Disorders
GNE, GPHN, HNRNPAB, HNRNPR, IGF2BP1, KIAA1549,
LAMP1, P2RX2, P2RX6, Pkc(s), PRKRA, RIF1, RPL35A,
S100A12, SYNCRIP, SYT3, SYT7, TBL1X
28
ARIH1, CARD14, CDH22, DOK5, EDA, ETS, ICOSLG, IL1RL2,
24
31
Humoral Immune
LINGO1, LTB, Lymphotoxin-alpha 1/beta 2, NFkB, NOL14,
Response, Lymphoid
PLEC1, POU2AF1, RAB3C, RBCK1, RNF216, RNF144B,
Tissue Structure and
RNF19B, SDS, SIAH2, SLC11A2, SLC37A4, SPIB, STK10,
Development, Post-
TNFRSF19, TNFRSF13C, TRIM9, UBE2, UBE2C, UBE2L6,
Translational
UBE2V1, WNT6, WNT10A
Modification
29
AP4M1, AXIN2, BBX, Bcl9-Cbp/p300-
24
31
Cell-mediated Immune
Ctnnb1-Lef/Tcf, CBFB, CCL18, CD6, CD58, CD3EAP, CLEC7A,
Response, Hematological
DEF6, ELAVL4, EOMES, Groucho, HIPK2, IL4, LAMP2,
System Development
LEF1, LEF/TCF, Mhc ii, MVK, NFATC2IP, NKX3-1, PKIB,
and Function, Immune
PTCH1, RUNX3, SLC26A2, SPDEF, SYT11, TBC1D17,
Cell Trafficking
TCF7L1, TLE4, TMEM131, TRA@, VTCN1
30
ACAP1, CCL19, CCL21, CDC16, COL8A1, CSF2, Csf2ra-Csf2rb,
24
31
Antigen Presentation,
CSF2RB, CUL3, DDX5, DDX54, EP400, EPC1, ESR2,
Cellular Movement,
Esr1-Esr1-estrogen-estrogen, ESRRB, EXOSC3, HNRNPD,
Hematological System
HOOK1, ILF3, ING3, IRAK3, KHSRP, KLHL12, MAD2L1,
Development and
NACC1, NR0B1, PRMT2, Rab11, RBM17, SNX20, TIA1, TIAL1,
Function
TRAP/Media, TRRAP
31
APLP2, C7ORF64, C9ORF100, CNTN1, DAZ4, DZIP1, ERK1/
24
31
Genetic Disorder,
2, GLYAT, HN1L, ID2, KIAA0182, LRRC41, MFNG, Notch,
Neurological Disease,
NOTCH2, NOVA2, NRCAM, PDLIM4, POU2F2, PSEN2,
Molecular Transport
PTBP2, QKI, RBPMS, RCOR3, Secretase gamma,
SLC5A7, Smad1/5/8, SMUG1, STRBP, TIE1, TNR, USH1C,
USH1G, XRCC6, XRCC6BP1
32
ANKRD44, BAP1, Cbp/p300, CYP19A1, DUB, ENO1, GATA4,
24
31
Organ Development,
LHX9, MUC4, NR5A1, SMAD2, Sox, SOX1, SOX3, SOX11,
Reproductive System
SOX12, SOX15, TBC1D1, TBX18, UCHL1, UCHL5,
Development and
Unspecific monooxygenase, USP3, USP6, USP10, USP14, USP25,
Function, Cancer
USP31, USP32, USP42, USP46, USP48, USP53, ZNF653,
ZNHIT6
33
CLN6, COL1A1, FLT1, FLT4, GIGYF1, GTF3A, IFI6, IFNGR2,
23
31
Cellular Movement,
IL20, IL23, IL12RB1, IL13RA2, IL31RA, IL8RB, NPY2R,
Cell-To-Cell Signaling
NRP1, NRP2, OMP, PAIP2, PEG10, PLXNA3, PRELP, RGS12,
and Interaction, Cardiac
Sema3, SEMA3B, SEMA3G, SOCS7, STAT3, TMF1,
Hemorrhaging
TMPRSS6, Vegf, Vegf
Receptor, ZBTB10, ZNF197, ZNF467
34
ABCC9, Ahr-aryl hydrocarbon-
22
31
Carbohydrate
Arnt, ALDOA, B4GALT5, CSE1L, EEF2, Esr1-
Metabolism, Gene
Estrogen-Sp1, FAM120A, HSP90AB1, HSPH1, KLF15, L-l
Expression,
actate dehydrogenase, LDHA, LDHB, LDHC, MIER1, NFIA,
Cardiovascular System
NFIC, Nuclear factor 1, NUMA1, PKLR, PKM2, POGZ, PSAT1,
Development and
RPL5, RPL7, RPS3A, RPSA, SFTPC, SFXN3, SLC11A1,
Function
SP1, TRIP12, XPO1, ZCCHC14
35
ARID1A, ATF7, BATF, BATF3, BAZ1B, CEBPE, CSTA, CYSLTR1,
22
30
Gene Expression,
CYSLTR2, EPB49, EVI1, FTH1, GATA1, GC-GCR
Cellular Growth and
dimer, HHEX, HIVEP3, Jnk dimer, JUN, JUN/JUNB/JUND,
Proliferation,
JUND, MAFF, MLLT6, MT2A, MTF1, NFE2L1, NRAP, PRRX1,
Hematological System
PXDN, SMARCA2, SMARCC1, SMARCD1, SRGAP3,
Development and
SWI-SNF, Tcf 1/3/4, TMC8
Function
36
ATP5B, CKAP4, CTNNA1, DNAH1, DNAJ, DNAJA1, DNAJA2,
22
30
Post-Translational
DNAJB11, DNAJC, DNAJC4, DNAJC10, DNAJC5G,
Modification, Protein
Gα q, GOT2, GPRIN2, HSP, Hsp22/Hsp40/Hsp90,
Folding, Cellular
HSP90AA1, HSPA5, HSPA8, HSPA9, KPNB1, KRT17, MAP3K7IP1,
Function and
NFE2L2, PITPNM3, PTK2B, SFN, SIL1, SYNPO2,
Maintenance
TNPO1, TRIM29, TUBB, TXN, XPO7
37
14-3-3, ARHGEF16, C12ORF51, CBLL1, CBY1, CD74, Cofilin,
22
30
Cancer, Reproductive
FAM62A, HAT1, HLA-DPB1, HLA-DRB4, HMG20B,
System Disease,
KIAA1429, MDM4, MHC II-&beta, Mhc2 Alpha, MYST3,
Neurological Disease
MYST4, PABPN1, PANK2, PAPOLG, REEP6, RPL4, RPLP2,
SH3BP4, SSBP1, STRAP, SUPT6H, TACC2, TPI1, TUBA4A,
TUBB1, TUBB4, Tubulin, YWHAZ
38
ADA, BIN1, C4A, DDX1, DMC1, Dynamin, E2f, ENG, FKBP3,
22
30
Cell Cycle, DNA
FMNL1, FOLR1, GOLT1B, HMOX1, IPO5, MCM5, MND1,
Replication,
NADPH oxidase, OBSL1, PFN1, Pld, PLD1, PRDX6, RAD51,
Recombination, and
RAD51C, RAD51L1, RALA, RANBP2, RIN3, RPS16,
Repair, Cell Morphology
SERBP1, SNCA, SYN2, TMEM126B, TYMS, tyrosine
kinase
39
ACP1, ADAM12, AFAP1L2, ANKRD11, CRLF2, DDR2, DGKA,
20
29
Cell Morphology, Cell-
DIAPH3, EPHA4, EPHA/B, Ephb, EPHB2, Ephb dimer,
To-Cell Signaling and
EPOR dimer, esr1/esr2, GPR172B, KBTBD2, KCNQ4, KDELR1,
Interaction, Cellular
NCK, NCKIPSD, NEU2, PIP, PTPN13, PTPN21, SDC2,
Assembly and
SERINC3, SH2D3C, SH3PXD2A, SKAP2, SLC16A1,
Organization
SRC, TNS1, WBP5, WWOX
40
ALK, ARHGEF15, BRAP, BRD7, C11ORF49, CENPF, CHKA,
20
29
Cancer, Cell Cycle, Cell
CTBP2, EFNA5, ENaC, EPHA, EPHA1, EPHA10, EPHB4,
Morphology
H19, IGF2BP3, IRS, ITPR3, KRT74, MAPK8IP3, MARK1,
MPRIP, NEFL, Pdgfra-Pdgfrb, PDLIM5, PI3K, PIK3AP1,
PIK3R2, RAP1B, RGL3, Rock, SCN7A, SCNN1B, SHC3,
SLC12A4
41
ADRA1B, ARMC2, CHMP4C, CLU, CP, Cyclooxygenase, FPR1,
20
29
Cancer, Reproductive
FPR2, GJC2, HDL, HMGA2, HPSE, IGHG1, IGKC, IL1,
System Disease, Free
Interferon
Radical Scavenging
beta, KIAA1409, KLK3, KRT7, KRT14, KRT16, MOCOS, Neurotrophin,
PAPPA, PKHD1, PNMA2, SAA1, SAA4, SAA@,
SCAF1, SLC2A3, SORCS1, SORT1, SPEF2, TTPAL
42
ATF6, BEX2, CALR, CCR4, CD8, CREBL1, ERO1L, GATA3,
20
29
Hematological Disease,
GOT, GZMA, HIST1H1T, HLA-
Immunological Disease,
E, HMGB2, HSP90B1, Ige, IL12, IL33, IL1RAP, IL1RL1, ITM2A,
Infectious Disease
LMO2, MHC Class
I, NASP, NHLH2, P4HB, PDIA2, PDIA3, PDIA4, PHOX2A,
POU3F1, SET, TAL2, Tap, TNFRSF25, TTLL5
43
ACTR2, ACTR3, ACTR1A, ACTR3B, AIF1, Arp2/3, ARPC5,
20
29
Cell-To-Cell Signaling
CDC42EP4, CFL2, CIAO1, CLCN3, CYFIP1, DIXDC1, FCGR1A/
and Interaction, Cellular
2A/3A, G-Actin, GIT2, GNL3, GOLM1, IQUB, KIAA1967,
Assembly and
MYLK3, MYO1D, NCKAP1, NWASP, PAK1, PCDH7,
Organization, Skeletal
PGAM1, Rac, RPL13A, TMEM33, TNFSF11, TROVE2,
and Muscular System
WAS, WASP, WIPF1
Development and
Function
44
adenylate kinase, AK2, AK7, AK3L1, Alcohol group accept
20
29
Amino Acid
or phosphotransferase, ATYPICAL PROTEIN KINASE C,
Metabolism, Post-
CDK6, CDK4/6, COQ7, DMPK, DYRK1A, FBXO7, HIPK1,
Translational
HSPE1, IRAK1, MAP2K2, MAP2K5, Map3k, MAP3K7, MAPK6,
Modification, Small
MAPK11, PAK2, PDK1, PDPK1, PKN2, PLK3, PPT1,
Molecule Biochemistry
PRKCH, PRPF4B, RPL29, Rsk, RTCD1, SH3RF3, TOMM70A,
WDR68
45
3 BETA HSD, BRF2, C2ORF47, CLEC11A, DCP2, EML4,
20
29
Free Radical
Fcer1, HIGD1A, HSD17B11, LRPPRC, Mek, MTR, NADH
Scavenging, Genetic
dehydrogenase, NADH2 dehydrogenase, NADH2
Disorder, Neurological
dehydrogenase
Disease
(ubiquinone), NDUFA1, NDUFA2, NDUFA4, NDUFA7, NDUFA8,
NDUFA12, NDUFA13, NDUFB2, NDUFB9, NDUFB11,
NDUFS1, NDUFS3, NDUFS8, NEK6, OTUD4, PTPLAD1,
SFXN4, STARD9, TSC22D1, UHRF1BP1
46
Alpha Actinin, BDNF, Calpain, CAPN6, CAPN10, CD37, CHRNA5,
20
29
Cellular Assembly and
CHRNB2, Clathrin protein, CLTB, CTSS, CXCL10,
Organization, Cellular
EPN1, EPS15, EPS15L1, GAD, HLA-DQB1, JARID2, LBP,
Function and
LDB3, MBP, MHC Class II, MYOZ2, MYOZ3, Nicotinic acetylcholine
Maintenance, Cellular
receptor, NTRK2, PEG3, PICALM, PTPN1, RFXAP,
Movement
RPS21, SCN5A, SCT, SNAP91, VIPR1
47
ADCY, ADRA1A, ADRB2, ADRB3, Beta Arrestin, BRS3,
20
29
Cell Signaling, Nucleic
C18ORF25, CFTR, CRHR1, CXCR4, DIO2, DZIP3, Galphai,
Acid Metabolism, Small
G-protein beta, GNA11, GNA12, GNA13, GNAQ,
Molecule Biochemistry
GNAS, GNB1, GNB1L, Gs-coupled receptor, HAX1, HGS,
HTR6, KCNK1, PLC, PLCD3, PLEKHA6, UBE2E1, UBE2G1,
UBE2I, UBE2J1, WSB1, XPNPEP3
48
C19ORF2, CCNT2, CDK9, DNA-directed DNA
20
31
DNA Replication,
polymerase, DNA-directed RNA polymerase, E3 RING,
Recombination, and
HLA-DQA1, HTATSF1, MDM2, MYCN, NCL, PCNA, POLE,
Repair, Gene Expression,
POLE4, POLH, POLR1D, POLR2E, POLR2J2, POLR2K,
Cell Cycle
POLR3C, POLR3E, POLR3G, POLR3H, PRIM2, RAD18,
RAD52, REV1, RFC3, RPA, RPL10, RPL37, RPS8, SSB, STK19,
TFAP4
49
ALP, ALPI, ALPL, ARHGEF5, BCL2L11, Bmpr1, BMPR2,
20
29
Carbohydrate
CAP2, CASP2, CRYM, DLX2, DLX4, DUSP14, FOXG1, FOXL1,
Metabolism, Lipid
Foxo, FSH, GAD1, GPRC5B, KRT84, LHX5, LMX1A,
Metabolism, Small
MAGED1, MSX2, OTP, PGRMC1, Pi4k, PI4K2A, PI4KA, Rb,
Molecule Biochemistry
SOX2, TCF3, TOX, TP53I11, UNC5A
50
ARHGAP4, ARHGAP6, ARHGAP8, ARHGAP29, ARHGDIA,
19
29
Cell Signaling, Cell
ARPC2, BAIAP2, CDC42, COL10A1, COL11A2, DGKQ,
Morphology, Cellular
DIAPH1, Erm, Gα 12/13, GRLF1, HPCA, IBSP, ICMT,
Assembly and
KIFAP3, MCF2L, MSN, MYO9B, Phosphatidylinositol4,
Organization
5 kinase, PIP5K1B, Ras homolog, RDX, RGMA, RHOA,
RhoGap, RHOH, RHOJ, SPN, srGAP, SRGAP1, SRGAP2
51
ATG5, ATXN3, CTSE, EMG1, Immunoproteasome Pa28/20s,
19
29
Cellular Assembly and
KHDRBS1, LCK, MAGEA3, NAT6, PMS2, Proteasome
Organization, Cellular
PA700/20s, PSD2, PSMA, PSMA1, PSMA5, PSMB, PSMB2,
Function and
PSMB6, PSMB7, PSMB8, PSMC, PSMC1, PSMC2, PSMC4,
Maintenance, Connective
PSMD, PSMD4, PSMD7, PSMD14, PTPN22, SNCAIP, SNX3,
Tissue Disorders
SOS2, WDR48, ZDHHC17, ZDHHC18
52
ACAC, ACACB, ACAT1, ADIPOQ, ADIPOR2, AMPK, Creatine
19
28
Lipid Metabolism, Small
Kinase, Cytochrome c, ELOVL6, ENSA, FABP, FABP2,
Molecule Biochemistry,
FABP3, FABP6, FOXO3, GYS2, HMGCR, Insulin, KHK,
Carbohydrate
LCP2, LIPE, MLXIPL, PLB1, POLDIP3, PRKAA, PRKAA2,
Metabolism
PRKAB1, PRKAB2, PRKAG1, SLC25A12, STX6, STXBP4,
UCP3, UQCRC2, UQCRH
53
AHSA1, ANAPC10, ANP32A, CALU, CAMK2D, Glycogen
19
28
Cancer, Cell Death,
synthase, HSPD1, MAP2K1/2, MID1, Myosin light chain,
Reproductive System
NEK1, NPTX2, PFKFB2, PP1, PP1/PP2A, PP2A, PPP1CB, PPP1R11,
Disease
PPP1R1A, PPP2R1A, PPP2R2A, PPP2R2C, PPP2R5A,
PPP2R5B, PPP2R5C, PSME2, Pyruvate
kinase, RAB18, RAB11A, RCN1, RCN2, SLC6A4, SLC8A1,
TMEM43, UPF1
54
ABCB4, ABCG4, AP4E1, APOA4, APOC4, ARG2, CD36, CNOT1,
19
28
Lipid Metabolism,
CNOT4, Coup-Tf, CPT1, CPT1B, CTNNBL1, CYP24A1,
Molecular Transport,
FXR ligand-FXR-Retinoic acid-RXR&alpha,
Small Molecule
GCN1L1, GPLD1, INSIG2, LTF, N-cor, NCOR-LXR-
Biochemistry
Oxysterol-RXR-9 cis RA, Nr1h, NR1H4, NR2F2, NUDT7,
PALB2, RANBP3, RARA, RQCD1, Rxr, SGOL2, SPP1, THRB,
UCP1, USF2
55
AGMAT, Arginase, BAT3, BIRC5, Caspase, DPT, DTX3L, FGF20,
19
28
Cell Death, Metabolic
FGFR3, IFT57, Ikb, JUP, KHDC1, KLF5, MAPKAP1,
Disease, Genetic
MST1, NGLY1, Nos, OTUD7B, PCCA, peptidase, PTPN14,
Disorder
RNF7, RNF130, SHFM1, STAM2, TH1L, TMPRSS11D, Tnf
receptor, TTLL3, UBC, UBE2D3, Ubiquitin, UBL7, ZNRF1
56
CD44, CSF1, CSPG4, DCN, ELN, FBN1, FCN1, FCN2, Fgf, Fibrin,
18
28
Protein Degradation,
GDF15, Gpcr, GPR68, Igfbp, KISS1, Mmp, MMP2, MMP7,
Connective Tissue
MMP12, MMP14, MMP15, MMP16, MMP20, MMP24,
Disorders, Genetic
NUAK1, PCDHGC3, PCOLCE, PCSK6, Plasminogen
Disorder
Activator, PLAU, PTPRO, SERPINE2, SPOCK3, Trypsin, VCAN
57
ALS2CR11, CD27, CD70, CNKSR1, DUSP4, DUSP6, DUSP9,
17
27
Cell Signaling,
DUSP16, E4F1, ELK4, GCKs, Il3r, Jnk, Jnkk, KIAA1217,
Embryonic
MAP1S, MAP3K2, MAP3K4, MAP4K2, MAP4K5, MAPK10,
Development, Tissue
MAPK12, MEKK, MEKKs, MGAT3, MINK1, MKP1/2/
Development
3/4, NET1, PRDX4, RASSF1, RHPN1, SERPINB3, TAOK3,
TEX11, TRAF
58
3′,5′-cyclic-nucleotide phosphodiesterase, AKAP, AKAP1,
17
27
Cardiovascular Disease,
AKAP5, AKAP10, Ap1, ARFGEF2, ARL3, C2ORF65, C3ORF15,
Visual System
Camk, CBFA2T3, CRX, ELL, GSS, NPHS2, NRL, OSGIN1,
Development and
PADI4, Pde, PDE11A, PDE4A, PDE4D, PDE4DIP, PDE6
Function, Kidney Failure
(rod), PDE6B, PDE6D, PDE6G, PDZK1, Pki,
PRKAC, PRKAR2A, PRKAR2B, PTGIR, SIRT2
59
CARS, CBX3, CDX4, CREB1, FUSIP1, GALNT1, GALNT2,
17
27
Post-Translational
GALNT5, GALNT7, GALNT8, Glutathione peroxidase,
Modification, Gene
GTF2A2, GTF2E1, GTF2F1, HPS6, MUC1, PDHA2, PEPCK,
Expression, Cellular
Polypeptide N-acetylgalactosaminyltransferase, RNA
Compromise
polymerase II, SLC18A2, SUB1, Taf, TAF1, TAF5, TAF11,
TAF1L, TAF9B, TFIIA, TFIIE, TFIIH, TGS1, UPP1, VSNL1,
ZEB1
60
BRAF, CD3, CD247, CFLAR, CRKL, DOCK2, DST, Dynein,
17
27
Nervous System
FKBP4, FLT3, GLMN, GRIA2, GRIA3, IKK, IL2RA, JINK1/
Development and
2, MAP1B, MAP4K4, NF2, NUDCD3, OIP5, PAFAH1B1, PCDHA6,
Function, Cancer,
POP7, PPIL2, Ptk, Rar, RELN, RPP30, SERPINB10,
Neurological Disease
SIRPA, SPIC, STAT5a/b, TCR, TUBB2B
61
ABCC3, ABCC4, ALDH, CAR ligand-CAR-
17
27
Drug Metabolism,
Retinoic acid-RXR&alpha, CES3, CITED2, CNOT3, CREBBP,
Vitamin and Mineral
CRYAA, CRYGC, CYP2C9, EID1, GST, HIST1H4E, HOXD10,
Metabolism, Amino
HS3ST1, HS3ST3A1, HS3ST3B1, MAST1, MIP, NCOA,
Acid Metabolism
Ncoa-Nr1i3-Rxra, NR1I3, PXR ligand-PXR-Retinoic
acid-RXRα, Retinoic acid-
RAR-RXR, SCAND1, SLC35A2, SS18L1, sulfotransferase,
SULT1A3, SULT1E1, SULT4A1, ZNF434, ZNF446, ZSCAN20
62
AGK, BDKRB2, C16ORF14, CASD1, CD48, CHRM2, DOK4,
17
28
Cell Signaling, Nucleic
ERK, FAU, G alpha, G alpha-G beta-GDP-G gamma, G
Acid Metabolism, Small
protein beta gamma, G-protein gamma, GNA14, GNAI1,
Molecule Biochemistry
GNAL, GNG2, GNG4, GNG7, GNG12, GPER, GPR1, GPR4,
KCNJ5, KLB, MPZL1, Plc beta, S1PR, S1PR2, S1PR3, S1PR5,
SPRED1, TFG, TNFSF8, UBA5
63
BGN, BMP, BTF3, CDKN2B, CK1/2, COL1A2, CSNK1A1,
17
28
Gene Expression,
CSNK1E, CSNK2A1, CSNK2A2, EGLN1, EID2, ENTPD5,
Cancer, Gastrointestinal
FST, Glucocorticoid-GCR, JPH3, NAP1L1, PEX6, PURB,
Disease
RFX2, RFX3, RNF111, Smad, SMAD3, SMAD4, SMAD5, Smad2/3,
Smad2/3-Smad4, SSRP1, SUPT16H, TEAD1, TFE3,
Tgf beta, TGFB2, YBX1
64
ATP2A1, ATP2A3, ATP2B2, C16ORF70, Ca2
16
26
Cell-To-Cell Signaling
ATPase, Calmodulin, CASK, CASQ2, CTNNA3, DLG1, F11R,
and Interaction, Cellular
GPR124, Guk, HR, JAM, JAM3, KCNJ10, MLLT4, MPP4,
Assembly and
MPP5, MPP7, PARD3, Pmca, PVRL4, RGN, Ryr, SERCA, SIPA1,
Organization, Cellular
SLC16A3, SSX2IP, T3-TR-RXR, TBR1, THRA, Thyroid
Function and
hormone receptor, TRDN
Maintenance
65
ADAM9, ADAM10, ADAM28, ADAM30, C5ORF13, CD9,
16
27
Cancer, Cell-To-Cell
CD53, COL13A1, F12, FN1, FYB, GAL3ST1, HTRA1, I
Signaling and
kappa b kinase, ICAM2, Integrin, Integrin alpha V beta
Interaction,
3, Integrinα, Integrinβ, ITGA4, ITGA9, ITGAE,
Hematological Disease
ITGB1, ITGB5, ITGB8, LACRT, LTBP1, LTBP3, Metalloprotease,
MIA, PACSIN3, PRAP1, Talin, VTN, WASP-
SLAP130-SLP76-VASP-NCK-VAV
66
Akt, CDH13, CEP55, CISH, COL4A3BP, CTF1, FKHR, IL11,
16
26
Cellular Growth and
IL6ST, INPP4A, IRS2, JAK, JAK1, LEPR, LIFR, LMO4, LPXN,
Proliferation,
MPL, PHIP, POM121, SCGB3A1, SEC14L2, SOCS, SOCS3,
Hematological System
SOCS4, SOCS6, STAT, STAT1/3/5 dimer, Stat3-
Development and
Stat3, TBC1D4, TPOR
Function, Inflammatory
dimer, UXS1, VPS37A, VPS37B, WSX1-gp130
Response
67
APOBEC3G, AZI2, BFAR, CASP1, CASP10, CD86, CELSR1,
16
26
Gene Expression,
CYB561, CYLD, IFIH1, IFN ALPHA RECEPTOR, IFN
Antigen Presentation,
Beta, Ifn gamma, IFNA4, IFNAR1, Interferon
Antimicrobial Response
alpha, IRF, IRF8, ISGF3, NF-
κ B, Oas, OAS2, PECAM1, PTGDR, RARRES3, RSAD2,
SF3A1, SGSM3, SLC2A11, TANK, TGM1, Tlr, TNFAIP3,
TRIB2, ZBP1
68
AMD1, BLK, BRSK1, C2, C1q, C1R, CARD8, CARD16, CD22,
15
27
Cell Death,
CD300A, CD79A, Complement component
Hematological Disease,
1, DLEU2, ENPP3, FCAR, Fcgr2, FCGR2A, HRG, IgG, IGHE,
Immunological Disease
IGHM, IGL@, Igm, IL21, INPP5D, LILRB1, MS4A1, NUP205,
PEAR1, SHC1, SHCBP1, SHIP, SHP, SYK/ZAP, TPR
69
ARVCF, CABP1, CaMK-II/IV, CAMK2A, CAMK2G, CaMKII,
15
25
Neurological Disease,
CDH2, CHRNE, CK1, Creb, EGLN3, Filamin, GPD1, GRIN,
Organismal Injury and
GRIN2A, GRIN2C, GRIN3B, LRRC7, MUC5AC, MYOG,
Abnormalities,
NMDA Receptor, NOX5, PACSIN2, PYGM, SDHD, SIX2,
Respiratory Disease
SLC32A1, Succinate dehydrogenase, Top2, Wnt, WNT3,
WNT11, WNT5A, WNT8B, WNT9A
70
ANKRD10, BARHL1, BAT1, C12ORF41, C3ORF26, CIRH1A,
15
25
Gene Expression,
CLUAP1, CNIH, DDX52, DHX15, DIMT1L, INTS2, MAGEA11,
Auditory Disease,
MBOAT2, MED13, MED23, MED28, MED7
Cellular Compromise
(includes EG: 9443), MIRN222 (includes EG: 407007),
MYBBP1A, PNN, POLR1D, POLR2C, POLR3A, PPIG, PRKRIP1,
PRPF4B, PSMC6, SFRS3, SMC4, SPRYD5, TCOF1,
TFIP11, TRMT1, WTAP
71
Actin, Alpha actin, ATPase, CALD1, CASC1, CCL5, CCL16,
14
24
Cardiovascular System
CD163, CEACAM1, Ck2, CTSB, CXCR5, DTNA, F Actin,
Development and
Mlc, MYH3, MYH7, MYH8, MYH14, MYL6, Myosin, Myosin
Function, Skeletal and
Light Chain Kinase, NAMPT, NFKBIL2, PRKG1,
Muscular System
SERPINB13, SQLE, STK17A, Tni, TNNI3, TNNT2, Tropomyosin,
Development and
Troponin t, TUBB2A, TUBB2C
Function, Tissue
Development
72
AKT3, C16ORF35, C5ORF13, CAPN6, CCNT2, FAM38B,
14
24
Nervous System
FBN2 (includes EG: 2201), FNDC3A, GSC2, JMJD1A,
Development and
KY, LGI2, LOXL3, MECP2, MIRNLET7A3, MIRNLET7F2
Function, Tissue
(includes EG: 406889), NNT, NRK, PCDH7, PTPN12,
Development, Cancer
RALGPS2, RET, RNF4, SCARA3, SDK1, SLC2A3, SMARCA1,
SNX25, TCIRG1, UBE2B, UBE2N, UBE2V2, UGCGL1,
XPO4, ZNF215
73
AFAP1L1, AK5, C17ORF28, C7ORF26, CDK9, CEP57, COL5A2,
14
24
Cell Cycle, Connective
E2F3, E2F4, E2F6, FRMD6, GCSH, H2AFX, HMGB1
Tissue Development and
(includes EG: 3146), KHDRBS1, MAGT1, MYCN, NCL,
Function, Gene
NMI, NUCKS1, PPIL5, RABIF, RBBP8 (includes
Expression
EG: 5932), RBL2, RFC3, RNF114, RPL22, RPS16, RPS23, RUSC2,
SRPR, TERC (includes EG: 7012), TERT, TRIM46,
UBASH3B
74
ABCC1, ARSA, ARSI, ATP6V0B, BTBD7, C16ORF84, C17ORF39,
14
24
Lipid Metabolism, Small
C2CD2L, C5ORF25, CCDC136, CEND1, CSPG4,
Molecule Biochemistry,
DCUN1D5, EMG1, EPC1, FABP3, FAM131A, IGSF9B, ILVBL,
Carbohydrate
IRF8, JMJD3, LARP4, MIRN337, MIRN130B
Metabolism
(includes EG: 406920), MIRN149 (includes EG: 406941),
MIRN205 (includes EG: 406988), MIRN28 (includes
EG: 407020), MUS81, RSF1, SAMD1, SFRS1, STBD1, SULF2,
SUMF1, WDR44
75
ARL17P1, C15ORF15, C20ORF43, CDK5RAP3 (includes
13
18
Gene Expression, Lipid
EG: 80279), CHERP, DNAJC30, GPR39, HNF1A, HNF4A,
Metabolism, Molecular
HOOK3, KIF9, MPZL2, MRPS23, OSBPL11, PRKAB1, retinoic
Transport
acid, SLC5A3, STX17, TTC25, TTC37, UFM1
76
B9D2, CNTN4, DCLK3, FBXO42, H2AFJ, HNF4A, INTS7,
13
17
Gene Expression, Amino
MLL5, MSRA, NAALADL2, NFYB, NFYC, PAQR5, RIMBP2,
Acid Metabolism,
SFRS17A, SHOX2, TTC39B, TTLL9, ZNF524
Cellular Development
77
ARHGEF12, CD200R1, DOK1, FGF6, FNBP1, growth
13
23
Cell Signaling,
factor receptor, Il8r, KNDC1, MRAS, MTG1, NGF, NRAS,
Hematological System
Ntrk dimer, P110, PDGF-AA, Pdgfr, Phosphatidylinositol 3-
Development and
kinase, PI3K p85, PIK3CD, PIK3R3, PLCE1, PLXNB1,
Function, Cancer
PTGFR, RAB3IL1, RAB3IP, Raf, Rap1, RAPGEF6, Ras,
RASAL1, RASGRP2, RASSF2, RASSF4, RIN2, SSPN
78
AKR1C2, ATIC, B3GNT1, CD9, CDC7, CDH2, CDK6, COL1A2,
13
23
Genetic Disorder,
COL6A1, COL6A2, COL6A3, CXCL10, Cyclin E, DBF4B,
Skeletal and Muscular
GAST, GGPS1, HNRPDL, Hsp27, LAMC2, MIRN362
Disorders,
(includes EG: 574030), MUC4, MUC5AC, NKD2, OSR2, PCSK1,
Dermatological Diseases
PLAT, SCCPDH, SCG5, SERPINB5, SLC39A14, TGFA,
and Conditions
TGFB1, TGM1, WISP3, ZNF236
79
ATF7, ATP4B, B2M, CGA, COMMD4, COMMD8, COMMD1
13
23
Amino Acid
(includes EG: 150684), CYLD, CYSLTR2, EMID2,
Metabolism, Molecular
ETS, FARS2, FOS, GABA, HMGB1 (includes
Transport, Small
EG: 3146), IL1R1, IRF8, MIRN128-2 (includes
Molecule Biochemistry
EG: 406916), NFATC1, NFKB1, NLRX1, PCNX, PSMC5, S100B,
SLC36A1, SLC39A13, SPAG9, SPP1, STX1A, SV2B,
TGFB2, TXLNB, UBE3C, VISA, ZNF503
80
ADAM10, ADAM19, ATM, CD44, CD1E, CH25H, CHI3L2,
12
24
Endocrine System
DCLRE1C, DDX5, EIF2S1, ESF1, GNL1, GPR126, GPR176,
Development and
HMGB1 (includes EG: 3146), HNRNPD, IKK, LAMA3,
Function, Nervous
LAMA5, LAMB3, LAMC2, MMP1 (includes
System Development
EG: 4312), PLAT, PLAU, PLG, PRKDC, RELB, RTP3, SDC4,
and Function, Organ
SLC35E1, TMEM49, TNF, TP73, TRIM15, ZNF330
Morphology
81
ABCB10, C14ORF172, C2ORF49, CHD1L, DEM1, EIF1AD,
12
16
Cell Morphology,
EXOD1, HNF4A, NOL7, SEC23A, SEC23IP, SLC22A3,
Cellular Assembly and
SLMO2, STAT1, STOML1, TMEM53, TRMT6, TTC22,
Organization, Cellular
ZAN
Function and
Maintenance
82
AHDC1, ATN1, ATXN1, BEND2, C17ORF81, GPATCH8,
11
18
Gene Expression,
HNF4A, IL34, IPO8, KIAA0664, KIAA0913, METT11D1,
Cellular Development,
MIRN135A1, MIRN135A2, MIRN135B (includes
Lipid Metabolism
EG: 442891), MTERF, MTERFD2, NR3C1, PHPT1, RAD54L2,
SLC1A6, TOX4, UNK, WNK2
83
ABCA13, ACOT8, ARNT, AZGP1, BNIP1, BTRC, CRP, DALRD3,
11
22
Gene Expression,
EPO, GATA2, GATA3, GATA4, IFNAR1, KCNH6,
Hematological System
KIT, L3MBTL, LMO2, NOS3, NOSIP, PDLIM2, PIM1, PTPN2,
Development and
RINT1, SEC22B, SLC24A1, STAT1, Stat1/3, TAL1, TCF12,
Function, Cellular
TYK2, UBE2F, USE1, ZDHHC8, ZDHHC21, ZW10
Development
84
Arf, CTTN, Cyclin D, EPS8L1, EPS8L2, Fgfr, FGFR4, FRS2,
10
21
Cell Morphology,
GAP43, Gsk3, HEY1, MCM6, MEF2, MEOX2, MTSS1, MYH11,
Cellular Assembly and
NEK3, NFAT complex, p70 S6k, Pak, PAX1, PDAP1,
Organization, Antigen
Pdgf, Pdgf Ab, PDGFB, PDGFC, phosphatase, PLC gamma,
Presentation
PRKD1, RPS6KB2, SAMM50, SCNM1, Sos, VAV, VAV2
85
ADAMTSL1, AGPAT4, ASCC3L1 (includes EG: 23020),
10
21
Cellular Development,
ATP5B, BAT1, C17ORF85, CCNY, CFL1, CLASP1, CRTC1,
Cellular Growth and
CRTC2, DHX15, DYRK1B, EPB41, FBXO44, IL3, IL17A
Proliferation,
(includes EG: 3605), KIF1B, KRT37, MEGF10, MIRN328,
Hematological System
MIRN205 (includes EG: 406988), MIRN211 (includes
Development and
EG: 406993), NCDN, NONO, OTX2, PARD3B, RNPS1, SFRS1,
Function
SMCR7L, TOP2A, YWHAB, YWHAG, ZFP36, ZNF295
86
ACCN4, ARIH1, C17ORF50, C1ORF9, C20ORF111, CHST11,
10
21
Carbohydrate
CTSK, EIF3M, ELK1, FATE1, HNRNPU, IFIT1, KCNAB3,
Metabolism, Small
MED13, MIRN349, MIRN142 (includes
Molecule Biochemistry,
EG: 406934), MIRN292 (includes EG: 100049711),
Vitamin and Mineral
MIRN298 (includes EG: 723832), MIZF, MYST3, OR8G1,
Metabolism
PAFAH1B1, PCGF3, SCUBE3, SEC14L4, SGK1, SLC23A2,
SPATA2, STAG2, TNIK, TRAF2, WAPAL, WDR38, XKR6,
ZFHX4
87
BDP1, BNC2, CBX3, CDCA7, CDK9, CDK4/6, CMAS, CSRP2,
10
21
Cell Cycle, Gene
CSRP2BP, CTBS, Cyclin E, ETV3, GTF2H4, HAP1,
Expression, Cancer
HCG 2023776, HNRNPC, ID1, IMPAD1, MFAP1,
MIRN187 (includes EG: 406963), MIRNLET7B (includes
EG: 406884), MTPN, MYBL2, MYC, NEUROD1, PDGFB,
RB1, RPS25, RRM2, SKP2, SLC35C1, SLITRK1, TYMS, UBAP2,
ZNF691
88
CACNA1I, CATSPER1, CATSPER2, CHIC2, EDEM2, ERGIC1,
10
21
Cellular Development,
GARNL1, HAND1, KITLG (includes EG: 4254),
Hematological System
LEF1, LRRN4, MAGEB2 (includes EG: 4113), MAGEE1,
Development and
MDFI (includes EG: 4188), MIRN129-1 (includes
Function, Hematopoiesis
EG: 406917), MIRN129-2 (includes EG: 406918), MYCN,
MYF5, MYOD1, MYOG, NCL, NR4A3, PARP1, PAX3, PHOSPHO1,
RABL5, RPL36A, RPS3A, TAL1, TCF3, TCF12,
TCF15, USP7, ZNF423, ZNF607
89
AGTRAP, ANK1, CACNB3, Calcineurin A, Calcineurin protein(s),
10
24
Cell Signaling,
CASR, DHRS9, ERLIN2, GABBR1, ITPR, ITPR1, ITPR2,
Molecular Transport,
JUNB, MEF2D, N-type Calcium Channel, Nfat, NFAT5,
Vitamin and Mineral
NFATC1, Peptidylprolyl isomerase, PGM2L1, Pkg, PLA2G6,
Metabolism
Pp2b, PPP3CA, RHD, SAMD4A, SCN3A, SCRIB, SPTAN1,
TRP, TRPC4, TRPV6, UBE4B, VitaminD3-VDR-
RXR, Voltage Gated Calcium Channel
90
ANXA8L2, AR, CCNE1, CREBBP, CYP1A1, CYP2B6
10
20
Gene Expression,
(includes EG: 1555), CYP2D12, CYP4B1, ETV1, GAS7,
Cancer, Cellular Growth
GHRHR, HOXD1, IQCK, KAT2B, KLK2, MIRN202
and Proliferation
(includes EG: 387198), MMP1 (includes EG: 4312),
MYCN, NCOA3, NDN, NUCB2, PLAC2, PLUNC, PRKACA,
PYCR2, retinoic acid, RPS12, SERPINA5, SERPINE1,
SMARCA4, SP110, THRA, TYSND1, ZNF673
91
ATP10A, ATP11A, ATP11B, ATP5B, B4GALNT4, BHLHB5,
10
20
Cellular Growth and
C10ORF2, C5ORF15, CAPRIN1, CDC25B, CDH20, CLIC4,
Proliferation, Connective
CRIP3, D4S234E, EPC1, FRAS1, GATAD2B, H3F3A
Tissue Development and
(includes EG: 3020), KIAA0907, Mg2+-ATPase,
Function, DNA
MIRN330, MIRN15B (includes EG: 406949), MIRN195
Replication,
(includes EG: 406971), MIRN22 (includes EG: 407004),
Recombination, and
NEBL, PCOLCE, PPM1G, PRKD1, PURA, RAB28, SEMA6A,
Repair
SFRS1, SPIRE1, YWHAQ (includes EG: 10971)
92
ABCD3, AFG3L2, C1ORF38, CIDEC, CTSK, CXCL9, ERAP2,
9
20
Skeletal and Muscular
FGF2, GAL3ST1, ganglioside
System Development
GD1b, Gsk3, IFI44L, IFNG, IL1RN, MEST, MON2, MRAS,
and Function, Tissue
MTMR3, MTMR4, Nos, ODC1, PI3, PRKDC, PTCD3, RNASE7,
Morphology, Tissue
SASH3, SDC4, SLC28A2, SPP1, TGFB2, TNFRSF11B,
Development
TNFRSF1A, VCAM1, ZFP57, ZFX
93
ACADS, ADAMTS2, ADSL, AQP5, CD36, CDC37, CHIA,
9
20
Cellular Movement,
CLCN7, CLIC2, DCN, FGF7, FOXF1, FPR2, FUT3, GAB2,
Cellular Function and
GATA2, HAS2, HDAC9, HOXA9, IRF8, ISG15 (includes
Maintenance, Organ
EG: 53606), LTBP2, MAP4K5, NRXN1, NXPH3, PDZD2, SDC4,
Development
SMAD7, ST3GAL3, TGFB2, TNF, TRIP6, UACA, ZBTB11,
ZNF107
94
ADM, AIFM1, BLM, CASP3, CDH2, COL4A3 (includes
9
20
Cell Death, Cell
EG: 1285), cyclic AMP, deoxycholate, EBAG9, EIF2AK3,
Signaling, Molecular
EMCN, EMILIN2, GALC, ganglioside GD1b, GBA,
Transport
GNAS, HSPB2, HSPB6, MAP4K1, MLH1, NEFM, NFE2L2,
PARG, PDE4A, phosphatidylserine, PRKD1, PRKDC, PSD3,
RBM5, RXFP1, S1PR5, SGPL1, SGPP2, sphingosine-1-
phosphate, ZC3H11A
95
BTRC, C15ORF27, CAND1, CDC34 (includes
9
20
Protein Degradation,
EG: 997), CDCA3, CDK9, CUL1, CUL7 (includes
Cancer, Immunological
EG: 9820), Cyclin E, DDX4, FBXL2, FBXL3, FBXL21,
Disease
FBXO2, FBXO4, FBXO6, FBXO7, FBXO10, FBXO15, FBXO18,
FBXO21, FBXO41, FBXW2, FBXW8, GCM1, GEMIN8,
KIAA1045, MIRN293, MIRN34B (includes
EG: 407041), RC3H2, RNF7, SENP8, SKP1, SKP2, ZC3HC1
96
ACE, ADAMTS7, CEP135, CPN2, CTSK, FGF20, FGF22, Fgfr,
9
20
Carbohydrate
FGFR1, FGFR4, Frizzled, GLI2, GPR1, heparan sulfate,
Metabolism, Small
heparin, KLB, LSAMP, NCAM1, NCAM2, NCAN, NDST2,
Molecule Biochemistry,
PAX3, PPIB, PRELP, PRNP, PTPRZ1, SFRP1, SMAD9, SPP1,
Cellular Development
ST8SIA3, ST8SIA4, THY1, TSPY1, WNT2, ZNF587
97
Beta-galactosidase, BMP7, BTRC, COX17, DOM3Z, DYNC2H1,
9
20
Digestive System
EEF1A1, EXOSC4, FAHD1, GATA4, GBA3, GLI2,
Development and
GLI3, HDAC9, HIPK2, KATNAL1, LCT, LEF1, MEF2A
Function, Gene
(includes EG: 4205), PIAS2, PIAS3, PSG9, SCAPER,
Expression, Cell
SENP6, SMAD2, SMAD4, SUMO1, TCTA, TMPRSS3, TRIM33,
Signaling
XIRP2, XPO5, XRN2, ZIC1, ZIC2
98
5′-nucleotidase, ABCA2, ABCD3, AFG3L2, Apyrase, ATP13A5,
9
20
DNA Replication,
ATPase, C21ORF77, CANT1, CNKSR2, CTSK, DDX1,
Recombination, and
DHX8, DHX16, ENTPD1, ENTPD2, ENTPD3, ENTPD5, HEATR3,
Repair, Nucleic Acid
KIAA0494, KIAA1279, KIF20B, MIRN200C
Metabolism, Small
(includes EG: 406985), MOBKL2B, NT5C, NT5C2, Nucleoside-
Molecule Biochemistry
diphosphatase, PSMC1, PTP4A3, TIMM10, TIMM23,
TMEM41B, TRPM3, WRNIP1, ZNF670
99
3-oxoacid CoA-transferase, ANKRD36B, ARL6IP5,
9
20
Lipid Metabolism, Small
C10ORF35, CAV1, DNAH1, DNAH5, DNAH9, DNAH10,
Molecule Biochemistry,
DNAH11, DNAH14, DNAL4, DPY19L1, DTX4, ELOVL7,
Genetic Disorder
FBLIM1, FILIP1, FLNA, FLNC, GPSM2, HRAS, HSD17B8,
JARID1B, MIRN31 (includes EG: 407035), MYOT,
NEWGENE 1560614, NIPA2, OXCT1, OXCT2,
PCYT1B, RPS6, RTP1, SCNN1A, TCTE3, TRIM54
100
ABHD5, AGPAT3, ANKRD27, C7ORF42, CETN2, DPP8, FAM153A,
9
17
Carbohydrate
FAM3D, FAM83H, HNF4A, LPHN3, METAP1,
Metabolism,
MIRN124-1 (includes EG: 406907), MIRN124-2 (includes
Dermatological Diseases
EG: 406908), MIRN124-3 (includes EG: 406909),
and Conditions, Genetic
MIRN297-1, M1RN297-2, PLDN, PLOD3, RBM47,
Disorder
SFRS12, SFRS2B, SYPL1, TARBP1, YBX1, YBX2
TABLE 5
Evidence for CDK2 pathway dysregulation
Gene/Protein
Status
Notes
CDK2
Up-regulated
Consistent with hypothesis
(19-fold)
PKCeta (PRKCH)
Deleted
Consistent with hypothesis
Rb
Unchanged
Neutral; activity regulated by CDK2 via
phosphorylation
E2F
Unchanged
Neutral; activity regulated by binding of
unphosphorylated Rb
Melanoma tumor samples and control normal tissue were obtained by biopsy. Whole exome sequencing (i.e. complete sequence of all transcribed genes) was done using commercially available Illumina technology. This method also provides quantification of individual mRNAs which provides a measure of gene expression analogous to whole genome transcription profiling.
A total of 2,802 genes from transcription profiling with a fold-change of +/−1.8 were passed on to network analysis. The filtered list of 2802 genes was subjected to an algorithm (Ingenuity Systems) that finds highly interconnected networks of interacting genes (and their corresponding proteins). The networks were integrated with the sequence data for mutated genes. Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways, which could provide potential therapeutic targets or, if possible, clusters of interacting proteins that could be targeted in combination for therapeutic benefit.
In addition to the dynamic networks created from the filtered genes, expression and mutation data are superimposed onto static canonical networks curated from the literature in a second type of network analysis that often yields useful pathway findings.
Before looking for potential therapeutic targets, an initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. The entire filtered list of genes was scored for associated cellular functions. The highest scoring category was Cancer, followed by Genetic Disorder (
In addition, the networks that were assembled by the protein interaction algorithm from the filtered list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:
Cellular Assembly and Organization
Amino Acid Metabolism
Post-Translational Modification
Cellular Movement
Hematological System Development and
Immune Cell Trafficking
Function
Protein Synthesis
Protein Trafficking
Genetic Disorder
Small Molecule Biochemistry
Cancer
Skeletal and Muscular
Disorders
Embryonic Development
This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue. Both of these classes of findings are consistent with global gene expression patterns of tumor tissue compared with normal tissue. These are very high-level general categories and by themselves do not point towards a therapeutic class. However they help to confirm that we are looking at signals from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is set forth in Table 6 at the end of this example.
The sequence data revealed an activating V600E mutation in the B-Raf gene. This mutation is relatively common in melanoma (Goel, et al., 2006) and leads to constitutive activation of B-Raf and downstream MAP kinase/ERK signaling pathway, which in turn promotes cell proliferation (Schreck and Rapp, 2006). The pathway associated with this is shown in
A second common finding in melanoma patients is the loss of the tumor suppressor PTEN. While PTEN was not mutated in the tumor sample, it was down-regulated approximately 5-fold. PTEN loss has been shown to activate AKT, which is also up-regulated in the tumor with subsequent activation of the mTOR pathway, a protein kinase pathway that results in the phosphorylation of p70S6K, 4EBP, RPS6 and EIF-4B. This pathway is shown in
Since both pathways converge at the level of cell proliferation, and it is known that targeting these pathways individually in patients with cancer has mixed results, it might be expected that targeting both pathways would be beneficial. In fact, because of the common co-occurrence of B-Raf mutation and PTEN loss in many melanoma patients, it has long been thought that B-Raf and PTEN might co-operate in the oncogenesis of melanoma and therefore targeting either gene individually might not be as therapeutically effective as targeting both. A recent study has shown that in a mouse model, inducing both B-Raf V600E and PTEN loss very potently produced melanoma which very closely recapitulated the human disease. Each genetic manipulation by itself was not as effective at inducing melanoma. Furthermore the tumors were effectively treated by a combination of a MEK inhibitor (MEK is downstream of, and activated by, B-Raf) and an mTOR inhibitor (mTOR is downstream of, and inhibited by, PTEN). Neither drug alone was effective (Dankort, et al., 2009). This implies that in patients with both B-Raf V600E activating mutation and PTEN loss, a combination of either a MEK or B-Raf inhibitor, and an mTOR inhibitor may be efficacious, and may explain why either drug class acting alone has had disappointing results.
Thus, the integration of two different genomic data sources revealed that two distinct but interacting pathways were dysregulated, and therefore potential targets. B-Raf was mutated but not transcriptionally regulated, and conversely, PTEN was down-regulated but not mutated. Either technology by itself (gene expression profiling or sequence data) would therefore have only indicated dysregulation of one of the pathways as a likely contributor to oncogenesis and tumor growth. Furthermore, the research literature provided an animal model validation for both these pathways being critical for melanoma development, and their combined inhibition being necessary for effective treatment. This type of integration also provides for a means of drug discovery via repurposing of existing drugs, as non-obvious findings will emerge from single patients which may be generalizable to other patients with similar pathway dysregulation.
While in the literature report described above a MEK inhibitor was used to inhibit the B-Raf pathway downstream of B-Raf itself, there are currently no approved MEK inhibitors, although trials are ongoing. It may therefore be possible to use a B-Raf inhibitor in combination with an mTOR inhibitor (which is also currently approved).
Although we did not see much up-regulation of genes downstream of B-Raf or PTEN, the effects of both of these genes on their downstream pathways occur at the level of protein phosphorylation and would not necessarily be expected to be reflected in changes in the mRNA levels of these downstream effectors.
In summary, the tumor sample displayed both an activating B-Raf V600E mutation, and down-regulation of tumor suppressor PTEN. Both of these pathways lead to cell proliferation, but targeting each individually has mixed results in cancer patients. There is literature evidence that targeting both of these pathways simultaneously may be effective in treating melanoma. Furthermore, there are approved drugs available which target both B-Raf (sorafenib), and mTOR (e.g., rapamycin) which is a downstream effector of PTEN loss.
In the foregoing examples, a mutation in the NRAS gene which could have activated the PI3K pathway and thus suggested combination treatment. However, this mutation was not characterized as to whether it would result in a loss or gain of function or neither. Since the mutation was not characterized, expression data were necessary to result in the suggestion of combination treatment.
These conclusions are validated by studies elucidating B-Raf pathway dysregulation at the protein level by determining whether downstream effectors of B-Raf are hyper-activated using immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to ERK1/2 and MEK, downstream effectors of B-Raf with respect to cell growth and proliferation, wherein hyperphosphorylation of ERK1/2 and MEK indicate over-activation by B-Raf and supports the validation of B-Raf as a target. PTEN at the protein level is also elucidated by determining whether downstream proteins normally inhibited by PTEN are hyper-activated. This can be done using immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to AKT and P6SK, pro-proliferative kinases in mTOR pathway normally inhibited by PTEN, wherein hyperphosphorylation of AKT and P6SK indicate over-activation of mTOR as a result of PTEN loss and supports the validation of mTOR as a target.
These are the complete networks of interacting genes generated from the filtered list of up- and down-regulated genes. The Score column represents the overall level of interconnectedness within each network, and the Top Functions column describes cellular functions that are over-represented (relative to chance) in each network, as determined by the individual annotation of each gene in the network.
TABLE 6
Regulated Networks from Tumor Sample
Focus
ID
Molecules in Network
Score
Molecules
Top Functions
1
C6ORF48, CACYBP, CAD, CFTR, COG2, COG3, COG8, DHX29,
31
35
Cellular Assembly and
DLG5, FAT1, GCC1, HNRNPU, HSPA6, IL1RAPL1,
Organization, Amino Acid
KDELR2, MLF1, MUC2, NDN, PDZK1, PHGDH, PHLDA2,
Metabolism, Post-
RCN1, SDF2L1, SEC61A1, SEC61G, SH3BGRL2, SLC1A5,
Translational Modification
SRP68, SRPR, SYNGAP1, TBC1D17, TNFRSF1B, TXNDC11,
XBP1, XPO1
2
ABTB2, BTN3A3, C11ORF82, CHST4, CTNNA-CTNNB1-
30
34
Cellular Movement,
CTNNG-CDH5, CTSF, DHRS3, ENDOG, FOXF2, KIF20A,
Hematological System
LAMP3, LSS, MCOLN1, MCOLN3, NFRKB, NINJ1, PDZD2,
Development and
PEMT, PLD3, PRKRIR, PXMP2, RASA3, SCUBE2, SERPINB8,
Function, Immune Cell
SLC14A2, SLC16A5, SLC1A4, SLC28A2, TDRD7,
Trafficking
TMEM49, TNF, TNFRSF21, UACA, ZNF330, ZNF365
3
AKR1B10, ANKRD35, ATXN10, C19ORF70, CDR2, CIRBP,
30
34
Protein Synthesis, Protein
COL24A1, EHD4, FAM113A, FOLR2, GSTK1, HIVEP3,
Trafficking, Genetic
HRK, JUN, LXN, MAFF, MAGEA6, MTHFR, OSTC, PHACTR3,
Disorder
PSPH, RPN2, SEC63, SERP1, SLC25A22, SNRPA1,
SRPRB, SSR4, STYXL1, SVIL, TCF20, Tcf1/
3/4, TNFRSF14, TSC22D1, VEPH1
4
ACTN4, ANKS1A, ASPM, CHCHD10, COL6A2, CRIP1, DZIP3,
30
34
Amino Acid Metabolism,
EMP3, ERBB2, ESPL1, IRX3, KRT7, LTBP3, MFNG,
Post-Translational
MICALL2, NPNT, NPTX2, NUCB2, P4HA1, P4HA2, P4HB,
Modification, Small
PDLIM4, PINK1, PMEPA1, PQLC1, Procollagen-proline
Molecule Biochemistry
dioxygenase, RAB18, SEMA7A, SH3BGRL, SHROOM3, SPAG4,
SRPX, ST3GAL4, TAX1BP3, WFS1
5
AASS, BMI1, CBX2, CBX7, CELSR2, COLEC12, Dolichyl-
30
34
Cancer, Skeletal and
diphosphooligosaccharide-protein
Muscular Disorders,
glycotransferase, EXOC1, FAM45A, HIST1H4C, HIST2H2AA3,
Embryonic Development
HIST2H2BE, IGF2BP2, KIAA1267, KIAA1797, KLF6,
MYCBPAP (includes
EG: 84073), NECAB2, PCGF6, PHF10, PHF19, PRRG2, PRUNE,
PXN, RRBP1, SERPINH1, STT3A, TBL1XR1, TES, TNS1,
TRIM9, TUSC3, U2AF1, VCL, VSX2
6
ABL1, Adaptor protein 1, ANKRA2, AP1M1, APC, BST2,
30
34
Gene Expression, Cancer,
C20ORF46, CADM1, CBX3, CBX5, CHPF, CRTAM, DDB1,
Gastrointestinal Disease
DIAPH1, DOK3, EPB41L3, FAM126A, GGA2, HDAC4,
HPRT1, KIF15, LRP3, MKI67, NECAP2, NR2C1, NXPH3,
PIGR, PIN1, PRTFDC1, PVRL3, SAE1, SH3BP2, TNFRSF11B,
WDR40A, ZIC2
7
ACTR10, ACTR1A, ANKS1B, CELSR3, DCTN2, DCTN3,
28
33
Cellular Function and
DST, DYNC1H1, DYNC1I1, Dynein, DYNLRB1, DYNLRB2,
Maintenance, Cellular
DYNLT3, FAM96B, INA, KHDRBS3, KNTC1, LDB3,
Assembly and
MYBPH, MZF1, NEFL, NIN, P38 MAPK, PDXDC1, PHLDA3,
Organization, Nervous
PNO1, RGMA, SAAL1, SFRS9, SGSM2, SPTBN2, TESC,
System Development and
TNNC2, TRA2A, ZW10
Function
8
AP3B1, AP3M1, ARNTL, BHLHB, BHLHE40, BHLHE41,
28
33
Cellular Assembly and
BUB1, CBLC, ETV5, KCNK3, LHX6, LMCD1, Mapk, MARCH2,
Organization, Cellular
NEUROG3, NKX2-1, OLIG2, PTCRA, PTPRH, PTRF,
Growth and Proliferation,
SFTPC, SGOL1, SPRY4, STRA13, STX7, STX8, THBS2,
Nervous System
TPD52L1, TRHR, VAMP8, VPS16, WDR91, WWTR1, XPOT,
Development and Function
ZNF148
9
BLOC1S1, BRWD1, CDCA7L, CORO2A, CPS1, DEAF1,
28
33
Gene Expression, Cell
DUSP22, ELMOD2, FASTKD5, HOXB1, ING3, KIAA1279,
Signaling, Amino Acid
MAL, MED4, MED25, MED30, NR3C1, PARP4, PLEKHF1,
Metabolism
PSMG2, RABGGTB, RRAGC, SEMA5B, SLC38A1, TADA1L,
TADA2L, Taf, TAF5L, TAF6L, TELO2, TNFAIP1,
TPST2, TRAP/Media, TRRAP, WDR40B
10
ABHD6, Adenosylhomocysteinase, AHCY, AHCYL1, AHCYL2,
28
33
Carbohydrate Metabolism,
APEH, APOBEC3B, CMBL, CTAGE5, DCPS, DCXR,
Small Molecule
EXOSC4, FAHD1, Hydrolase, IER3IP1, IMPA2, KCTD12,
Biochemistry, RNA
LRRC8D, MLEC, MRTO4, NHP2L1, NLN, PFAS, PPM1G,
Damage and Repair
PTPN7, REXO2, RHPN2, STRADB, TARSL2, TMEM51,
TMPRSS3, TRAF6, TSEN15, TUBB6, ZRANB1
11
C17ORF70, CCL5, ECEL1, EI24, FANCD2, FANCL, FAR1,
28
34
Endocrine System
FOXC1, HOXA5, HOXB3, HOXD12, IER2, KLF10, MEIS1,
Development and
MEN1, MLKL, NELF, NKX2-5, PBX1, PBX2, PHC1, PTGDS,
Function, Organ
RAD51AP1, RNA polymerase II, SETD2, SFMBT1,
Development, Organismal
SLC9A8, SMARCD3, SMYD3, TARBP1, TBX1, TBX2, TSPAN7,
Development
ZDHHC7, ZNF83
12
ACAA2, AGPAT9, ANXA5, Apyrase, ASB13, EIF3A, EIF4EBP1,
27
33
RNA Damage and Repair,
EIF4G1, ENTPD3, ENTPD6, ENTPD7, F5, FGFR3, FGFR4,
Organ Development,
GPRIN2, GRAMD3, HOXC9, KIAA0247, LAMP2,
Respiratory System
LDLRAD3, LIF, LOXL4, Nucleoside-diphosphatase, NUDT9,
Development and Function
OSBP2, PROS1, SELENBP1, SERTAD2, SMG7, STAMBPL1,
TMCC2, TRIP13, UPF2, VWF, ZFP36
13
AFF3, BAT2, C4ORF14, COL9A1, COL9A2, DDX20, DDX47,
26
32
RNA Post-Transcriptional
DICER1, EIF2C2, H1F0, HSP90AB1, HSPH1, HYOU1,
Modification, Cancer,
Importin beta, IPO8, IRS1, L-lactate dehydrogenase, Ldh,
Respiratory Disease
LDHA, LDHAL6A, LDHAL6B, LDHC, LSM10, NFIB, NPAS2,
PES1, PIWIL4, RAD51L1, SCML2, SND1, SNRPD1,
SNRPF, SNRPG, STARD13, TNRC6B
14
BAAT, COL15A1, Delta/Jagged, DLL1, DLL3, EHD1, FBLN1,
26
32
Cellular Development,
GIPC1, GRB10, HES1, HES6, HYAL2, ID2, IGF1R, KCNA3,
Cardiovascular System
KCNAB2, KLF2, LAMA4, MEGF10, MFAP5, MIB2,
Development and
NOC2L, Notch, NOTCH2, NOTCH4, NOV, OLIG1, RPL24,
Function, Tissue
SDC4, SH3BP4, SLC7A8, SLCO2A1, TFPI, Troponint,
Morphology
TYMS
15
ADAM19, Alpha catenin, ASAP1, BXDC2, C20ORF30, CAM,
26
32
Cell-To-Cell Signaling and
CLDN7, CLDN12, CNTNAP2, CTNNA1, DDX27, DHX57,
Interaction, Cellular
DKC1, GNL3, KIF14, LYAR, MARCKSL1, MPDZ, NEDD9,
Assembly and
OSTF1, PACSIN3, Pseudouridylate synthase, PSTPIP1,
Organization, Cancer
PTK2B, PTPN3, PUS1, PUS3, RPL28, RPUSD4, SH3KBP1,
SRP14, TJP2, TJP3, TRUB1, VEZT
16
AATF, CAMP, CCL2, CHGB, CSPG4, DAPK3, DNA-directed
26
32
DNA Replication,
DNA polymerase, EYA1, EYA4, GTF2F2, HMGB2, KIAA0101,
Recombination, and
MPHOSPH6, MPZ, PAFAH1B3, PAWR, PKM2,
Repair, Cellular Assembly
PLEKHM1, PMP22, POLB, POLD1, POLL, POLQ, POU3F1,
and Organization, Nervous
REV3L, SIX1, SIX4, SIX5, TFIIF, TFIIH, TLE1, TLE3, TNFAIP2,
System Development and
UTX, XRCC1
Function
17
Alcohol group acceptor phosphotransferase, ARID2, ARID1A,
26
32
Cell Cycle, Amino Acid
ARID4B, BRAF, CDK8, EFCAB6, EVI1, GRK4, GSPT1,
Metabolism, Post-
HDAC1, LIMK1, MAD1L1, MAP2K3, Map3k, MAP3K6,
Translational Modification
MAP3K8, MAPK9, MECP2, MEKKs, NDC80, NEK2, PAK1,
PAK2, PCTK3, PRKCQ, PRKX, RCOR2, SALL1, SALL4,
SAP18, SMARCE1, SPC25, TBX3, TTK
18
AGL, ALP, ANK3, BMP, BMP4, BMP7, BMPR2, C10ORF54,
26
32
Cell Signaling, Cell
COL7A1, CXORF15, DYNC2LI1, EID2, FST, ID1, ID3, JPH1,
Morphology, Cellular
KLHL9, KRT2, LAPTM5, NEDD4L, NELL1, PAX9, PDZRN3,
Development
PYCR2, RASD2, RSPH3, RUNX2, Smad, SMAD3,
ST6GALNAC2, TAGLN, TWSG1, ZC3H7A, ZEB2, ZMIZ1
19
ADAMTS2, ADAMTS4, ADAMTS7, APH1B, ATG4C, BIRC6,
26
32
Protein Degradation,
BLMH, BMP1, CASP4, CASP6, COL4A1, COMP, CSF1R,
Connective Tissue
CSF2RB, CTSD, Cytochrome c, DIDO1, FAP, GRN, GZMB,
Disorders, Dermatological
ICAM5, JUP, LTA4H, MDN1 (includes EG: 23195),
Diseases and Conditions
MT1A, PCSK7, peptidase, PSEN2, PSMB5, RNF150, UBE2,
UBE2E2, UBE2F, UBE2L6, UBE2S
20
CITED2, CMPK1, CNPY2, DSTYK, DUB, FIBP, GZMM, ITIH5,
26
32
Behavior, Digestive
KIF23, LHX2, MIF, MSRB2, MYCN, NMU, NMUR2,
System Development and
OTUB1, PARP1, PDE4DIP, PLA2, PLA2G2A, PLA2G4D,
Function, Cell Morphology
RASGRF1, TGM2, TMEM87B, TNIK, UCHL1, UCHL3, Uridine
kinase, USP6, USP8, USP10, USP48, USP53, USP54, ZFAND5
21
ANK1, BRPF1, CA2, CA3, CA7, CA14, CA5B, Carbonic
26
33
Genetic Disorder, Renal
anhydrase, CKS2, COPS6, CRELD1, CSRP1, CSTF3, CTDSPL,
and Urological Disease,
DLGAP5, DRAP1, FBP1, Fructose 2,6 Bisphosphatase,
Infectious Disease
GGT1, HEXB, MAP7D1, MXD4, PFKFB2, PFKL, PFKM, PGM1,
PLOD1, PTEN, PTPRZ1, PYGL, SLC4A2, SLC4A3,
STK40, SURF4, TCP10L
22
ADAMTS5, AIF1, ALT, ANXA9, CARD8, CASP1, CASP5,
26
32
Lipid Metabolism, Small
Casp1-Casp5, CEBPG, CHST6, CP, F13A1, GM2A, GPT2, IFT57,
Molecule Biochemistry,
IL1B, IL1F7, ITPA, LCP1, MIA, NALP, NLRP1, NLRP2,
Genetic Disorder
NLRP6, PELI1, PLA2G7, PLB1, PPHLN1, RARRES2, RNASE7,
SLC25A25, SMPD1, SRGN, TPSD1, TYMP
23
3′,5′-cyclic-nucleotide phosphodiesterase, ARL2, BASP1,
26
32
DNA Replication,
Calmodulin, CAMK1, CAMKK2, CCT2, CCT5, CDYL, DHX38,
Recombination, and
FAM10A4, IQCB1, LIMA1, LNX2, MAP3K3, MYO1B,
Repair, Nucleic Acid
MYO1D, NRGN, NUMBL, Pde, PDE1B, PDE2A, PDE5A,
Metabolism, Small
PDE6B, PDE6D, PDE7B, PDE8B, PPP4C, RAI14, RIPK3,
Molecule Biochemistry
RPH3AL, SCIN, TRPM2, TRPV4, UNC13B
24
APPL2, BAT5, C19ORF10, DTX3L, GPRC5C, IFI27, IFIT2,
25
32
Gene Expression, Lipid
IFIT3, IFITM1, Immunoproteasome Pa28/20s, KPNA5, LY6E,
Metabolism, Small
NCAPD3, NCAPH2, ODF2L, PARP9, PDLIM2, PILRB
Molecule Biochemistry
(includes EG: 29990), PRSS23, PSMA2, PSMA6, PSMB,
PSMB7, PSMB8, PSME2, RNF5 (includes EG: 6048), SCP2,
Soat, SOAT1, SOAT2, SP110, STAT1, STAT2, TMEM147,
TMEM222
25
ABL2, ALOX5AP, Ant, Basc, BCL2L1, BCL2L10, BLM, BNIPL,
25
31
Cellular Function and
BRIP1, CEBPZ, CHEK1, CHTF18, DSCC1, ERCC1,
Maintenance, DNA
Mre11, PAXIP1, RAD50, RAD9A, RFC2, RFC3, RPA, RPA3,
Replication,
RTKN, SEMA6A, SIVA1, SORBS2, SRPK2, TERF2, TERF2IP,
Recombination, and
TIMELESS, TIMP4, TP53BP1, WISP1, XRCC5, XRCC6
Repair, Cell Cycle
26
ADARB1, ADD3, AXIN2, CCNO, CTH, CXCR7, DTNA, ELOVL2,
25
31
Amino Acid Metabolism,
GDF15, GHR, GREM1, growth factor receptor, HSD3B7,
Small Molecule
KCNJ12, MCFD2, MT1E, MTHFD2, NOX4, PDGFBB,
Biochemistry, Cell Death
PDGF-AA, PLEKHA1, PRRX2, PSAT1, RND3, SLC1A1,
SNTA1, SNTB1, SNTB2, SNURF, Sphk, SYNC, SYNE1,
TRIB3, UCK1, VAPB
27
ASB9, C4ORF17, CDC14B, CKB, CKM, Creatine
25
31
Small Molecule
Kinase, ENO1, ENO2, Enolase, GTF2IRD1, HERC5, KRT78,
Biochemistry, Cell-To-Cell
MAP1B, MEF2C, MYOM2, NEBL, NGF, RANBP1, ROR1,
Signaling and Interaction,
RUSC1, SCG2, SERPINF, SERPINF1, SERPINF2, SH3PXD2A,
Cellular Assembly and
SIGIRR, SIRT2, SNX22, STK38, SYNPO2, TRAF3IP1,
Organization
TUBA4A, Tubulin, VGLL4, ZYX
28
AKAP6, ALS2CR12, BCLAF1, Cdc2, CLK1, Cyclin B, DARS,
25
31
Gene Expression, Cell
DNA-directed RNA polymerase, FKBP5, FKBP6, FKBP11,
Cycle, Cell Morphology
FLNB, GALK1, HMG20B, KIF4A, LATS2, LPHN1, LPHN2,
Peptidylprolyl isomerase, PIK3R1, PIN4, POLR1B,
POLR1E, POLR2D, POLR2F, POLR3A, PPIG, PPIL3, QRICH1,
ROS1, SFRS4, SHANK2, TACC2, TAF1C, TCOF1
29
BCL10, BIRC3, BIRC8, BRCA1, C11ORF9, C9ORF89, CARD10,
25
31
Cell Death, Cancer, Cell
Caspase, CCNG1, CD80/CD86, CHST1, CTCFL, DLAT,
Cycle
E3 RING, HERPUD1, HIST1H2BC, KHDC1, LSP1, MLXIP,
MX1, NT5DC2, NUFIP1, P2RX1, PDCD6, PKC(β,
θ), PLAG1, PLSCR4, STC1, SUGT1, TNFRSF18,
TNFRSF19, TRAF1, WEE1, WIT1, ZNF350
30
CD34, CRYAB, CTSL2, CUBN, DUSP6, Dynamin, ENC1, FSH,
25
31
Organ Development,
FSHR, GATA2, GPRC5B, HAS2, hCG, HPSE, HSD17B,
Reproductive System
HSD17B1, HSD17B4, HSPB2, LHCGR, LRRC32, MAMLD1,
Development and
MARCH3, MT1H, MT1X, MYO1E, PLA1A, RGS5, SETX,
Function, Developmental
SLC20A1, STEAP1, STK17A, TF, TFRC, TSHB, VGF
Disorder
31
ADRB2, BSCL2, C9ORF46, CHD3, Ck2, Clathrin, COL1A1,
25
31
Cancer, Tumor
COL1A2, CSPG5, CUX1, DNASE2, DRD5, DSPP, FAM134A,
Morphology, Molecular
FAM176A, FAM86C, Gs-coupled
Transport
receptor, Hat, IRF4, NEIL2, ODC1, PAM, PLA2R1, PRDX5,
RBM17, SAT1, STARD10, TGFB3, TNP1, TOP2A, TSC22D3,
TSPAN13, UBQLN3, YBX1, YBX2
32
APP, ATOX1, ATP7A, C9ORF75, CLIC1, CLSTN1, ETHE1,
24
33
Cellular Function and
EVI5L, FAM160A2, GLUL, HSD17B14, KCTD13, Na-k-
Maintenance, Small
atpase, NUDT18, PCBD1, PDIA6, PERP, Plasminogen
Molecule Biochemistry,
Activator, PPME1, PPP1R13B, RAB38, RAB33A, RABAC1,
Molecular Transport
RENBP, RTN2, RTN3, SDPR, SNX15, SORL1, SPON1, SRGAP3,
TM2D1, TP53BP2, TP53I3, ZBED1
33
ALAD, ARID1B, BBS1, BBS7, CCL8, CCL23, CCR1, EDEM1,
24
31
Cellular Assembly and
FLOT1, GNA14, HES5, HSCB, IFI30, IFI35, IFITM2, IL23,
Organization,
IL24, IL11RA, IL20RA, MAN2A1, MAN2A2, Mannosidase
Developmental Disorder,
Alpha, NIPSNAP3A, NMI, PCM1, PCNT, PLP2, RNASE1,
Genetic Disorder
RRP12, SOX10, STAT3, STAT1/3/5/6, THY1, TRIP10,
WASP
34
ANTXR1, ATF5, BATF, BATF3, BCL11A, BRD7, CEBPB,
24
31
Gene Expression, Cancer,
CREB5, CREB3L4, Ctbp, DBP, DDIT3, DFF, ELL3, F8, F8A1,
Hematological Disease
FOSB, FTH1, HIPK2, LCN2, MAGEH1, NFIL3, NR4A2,
ORM1, PER3, Pias, PIAS3, PML, SATB2, SLK, TDG, TM4SF1,
Top2, TP53INP1, UPP1
35
ACP1, AK3L1, ARSB, ARSG, ARSH, ARSJ, ARSK, Aryl
23
30
Cellular Movement,
Sulfatase, BCAT2, CST2, ENSA, GART, HEY2, HIF3A, JAG1,
Skeletal and Muscular
KCTD15, NCK, Pak, Pdgf Ab, PDGF-
System Development and
CC, PDGFC, PDGFD, PDGFRB, PPA1, PPP1R15B, SEMA3B,
Function, Gastrointestinal
SMTN, ST5, STARD8, SULF1, SULF2, SYNM, TBC1D8,
Disease
VEGFA, WIPI1
36
Acid Phosphatase, ACP5, ACP6, AHCTF1, CENPF, CPNE2,
23
30
Molecular Transport, RNA
CPNE4, DHCR7, IBSP, KNDC1, LIMS1, LIMS2, Mi2, MYCBP2,
Trafficking, Cellular
MYO1C, NUP133, NUP160, NUP214, NUPL1, PARVG,
Growth and Proliferation
PKC (α, β, γ, δ, ε,
ι), POU3F2, POU3F4, PQBP1, RAD21, Ras, RASAL1,
RDX, RGS10, RSU1, SEC13, SGPP1, SMOC2, Tap, TM7SF4
37
ABCC6, ABCD3, ACTC1, adenylate kinase, AFG3L2, AK1,
23
30
Energy Production,
AK5, ATP11B, ATP13A5, ATP1B1, ATP5D, ATP5G1, ATP6V0A2,
Nucleic Acid Metabolism,
ATP6V1E2, ATP6V1G2, ATP6V1H, ATPase, BAX,
Small Molecule
COX7A1, CTSK, Cytochrome c oxidase, ETNK2, H+-
Biochemistry
exporting ATPase, H+-transporting two-sector
ATPase, HTR2B, MFN1, MYH1, MYH6, MYL1, MYO9B,
NOMO1, PSMD6, SMARCA4, TNNI2, TRIM63
38
ACACB, AMPK, ANGPT2, ANP32B, APOB, CCNG2, CDH1,
23
30
Cellular Function and
CSNK2A2, FKBP4, GTSE1, Hsp27, HSP90AA1, HSPA2,
Maintenance, Cellular
HSPA5, HSPA1A, HSPA1B, KCNMA1, KLK3, KLK6, LOXHD1,
Compromise, Cell-To-Cell
NAP1L4, Ndpk, NME1, NME2P1, Nos, PA2G4, PRKAA,
Signaling and Interaction
RBM4B, RPL30, RRM2, TMEM132A, TTLL4, TXNDC3,
ZBTB33, ZEB1
39
3 BETA HSD, ALOX5, ASCC2, ASS1, CHUK, CLEC11A,
23
30
Cell Signaling, Cell-
DHH, EGR2, EGR3, FAM118B, GJB1, GLI1, Gli-Kif7-
mediated Immune
Stk36-Sufu, GMFG, HHIP, IDS, KIF7, LASS4, Mek, NAB1,
Response, Cellular
NCL, NDRG1, NXPH4, Patched, PPP2R2B, PPT2, S100A9,
Development
SELE, SH3D19, SHMT2, SNX10, STK36, Tnf
receptor, TNFSF13B, TUBB4
40
ALS2CR11, BRD8, CCDC5, CCDC76, CDC45L, CDCA4,
23
32
Cell Cycle, DNA
CDT1, CREG1, CSDE1, DUOX2, E2f, E2F2, EP400, ERRFI1,
Replication,
HIST1H3H, L3MBTL, MCM3, MCM4, MCM5, MCM6,
Recombination, and
MCM7, MCM10, MCPH1, MTSS1, NASP, NOM1, NRD1,
Repair, Gene Expression
NUSAP1, OIP5, Pdgf, PNKP, Rb-E2F transcription
repression, SETD8, TCEB3B, ZBTB43
41
ADH4, AIFM1, BSN, C6, C8, CAPN1, CAPN6, CAPN9, CAST,
22
31
Lipid Metabolism, Small
CDK5R1, CDK5R2, CTSC, CYB5R1, CYB5R2, DHRS1,
Molecule Biochemistry,
DHRSX, Electron-transferring-flavoprotein dehydrogenase,
Vitamin and Mineral
ETFDH, IRF2, LPA, MAPRE2, MLPH, Oxidoreductase,
Metabolism
P2RY2, PDIA5, RDH, RDH5, RDH8, RDH11, RETSAT, RIMS1,
SLC35C2, SLC9A3R1, TBC1D10A, VCAM1
42
AHSG, AKT1, ARL15, BPGM, BRSK1, C1QC, CCDC106,
22
30
Nucleic Acid Metabolism,
CDCA3, CRMP, CRMP1, DDX18, Dihydropyrimidinase, DPYS,
Cell Morphology, Renal
DPYSL3, DPYSL5, FES, FXYD5, HNRNPH3, KIAA1199,
and Urological System
LRRC1, MICAL1, MYT1, NUAK1, PKC ALPHA/BETA,
Development and Function
Plexin A, PLXNA1, PLXNA2, PLXNA3, PRPSAP1, Sema3,
SEMA3C, SEMA3F, SIK1, STRADA, UBE4B
43
CABLES1, CDK2, CKS1B, Glutathione peroxidase, Glutathione
21
29
Cell Morphology, Genetic
transferase, GMFB, GPX8, GSTA2, GSTM1, GSTM2,
Disorder, Hepatic System
GSTM4, GSTO1, GSTO2, GSTP1, GSTT1, IGSF21, Laminb,
Disease
LMNA, LMNB1, MGST2, MGST3, MRFAP1L1, MYH8,
NPDC1, PDCD11, PKC (α, β, ε, γ),
PKC (α, ε, θ), PRKCA, PRKCH,
RAB17, S100A8, Sod, TAGLN2, TSNAX, UNC84A
44
ALDH2, ANAPC13, APC, AURKA, BCKDHA, BCKDHB,
21
29
Cell Cycle, Embryonic
CD3EAP, CDC20, Creb, CSN3, ETV1, FBXO5, GALNT3, GALNT8,
Development, Cell-
GALNT11, GALNT12, GALNTL4, GRIN, GRIN2C,
mediated Immune
GRIN2D, HSPE1, KRT1, MAPK11, MSK1/2, MUC5AC,
Response
NOX5, PKMYT1, Polypeptide N-
acetylgalactosaminyltransferase, PRKD1, PTTG1, RPS6KA2,
RPS6KA4, Rsk, STEAP3, UBE2C
45
Ahr-aryl hydrocarbon-Arnt, ANXA4, ARMET, CYP1A1, CYP2J2
21
29
Cardiovascular Disease,
CYP4A22, CYP4F11, CYP4F12, ELF4, EPHX1, GPC1,
Genetic Disorder,
MYO10, NADH dehydrogenase, NADH2 dehydrogenase,
Neurological Disease
NADH2 dehydrogenase (ubiquinone), NDUFA4L2, NDUFB6,
NDUFB7, NDUFB8, NDUFC2, NDUFS3, NDUFS4,
NDUFS5, NDUFS7, NDUFS8, NDUF V1, NDUFV2, NDUFV3,
NOS3, NOSTRIN, Nuclear factor 1, TACC3, TRIP11,
TUBA1A, Unspecific monooxygenase
46
ADPGK, AKAP12, ALPHA AMYLASE, AMY1B, AMY2B,
21
29
Cell Cycle, Cancer,
CCNE1, CD38, CDKN1C, CTSH, Cyclin A, Cyclin D,
Reproductive System
Cyclin E, DMXL1, E2F5, EPAS1, FRAP1, GAL3ST1, GBE1,
Disease
IL6R, LGALS3, LYZL1, MB, MEF2, NRAS, NRN1, NUDT10,
OMA1, Pld, PLEK2, PRR5, RASSF1, RIN2, SKP2, SLC16A3,
ZBTB17
47
14-3-3, ADARB2, CRABP2, CXCL10, CXCR4, DHDDS, EMID1,
21
29
Embryonic Development,
GAD1, GPR1, IL17R, IL17RA, INTS3, LCT, LRRC37A3,
Tissue Morphology,
MAP2K1/2, MARK1, MYH2, NRIP1, PAX3, PRAME
Dermatological Diseases
(includes EG: 23532), PRDM5, PSG2, PTPRJ, RARA, RARB,
and Conditions
Rbp, RBP5, RBP7, SLC26A4, STAT5a/b, SYNCRIP, TG,
TGM1, TNFRSF10B, Transferase
48
ABR, ARHGAP27, ARHGDIB, Arp2/3, BAIAP2, CNTN1,
21
29
Cellular Assembly and
CNTNAP1, DEF6, DOCK2, EFNB3, EPB41L2, EPS8, EVI5,
Organization, Nervous
FCGR1A/2A/3A, Integrin alpha V beta 3, ITGB5, PARD6G,
System Development and
Phosphatidylinositol4,5 kinase, PIP4K2A, PIP5K1A, Plexin
Function, Cell Morphology
B, PLXNB1, PREX1, PTPRB, Rac, RAC1, RAC2, RAPGEF1,
RASSF2, RGL2, RIT1, RRAS, SEMA4D, TIAM2
(includes EG: 26230), TNK2
49
BGN, BMS1, C2, C1q, C1QA, C1S, CCNB2, Collagen(s), Complement
20
30
Cell-To-Cell Signaling and
component
Interaction, Connective
1, DCN, FBN1, FMOD, FSTL1, Igm, KIAA1191, LAMA3, LSM4,
Tissue Development and
NCR3, NOL12, PLEKHA5, PTS, RCBTB1, RORC, SF3B3,
Function, Skeletal and
SFRS15, SFTPD, SLC25A38, SNRNP70, SNRPB2, SNRPN,
Muscular System
Tgf beta, TGFBI, TLL1, TLL2, WBP4
Development and Function
50
B4GALT1, B4GALT4, B4GALT5, B4GALT6, CD9, CD81,
20
28
Dermatological Diseases
CDC42EP5, CSF3R, DUSP10, DUSP16, Erm, ETV4, Galactosyltransferase
and Conditions, Genetic
beta 1,4, Gm-Csf Receptor, GPR56, Integrin
Disorder, Carbohydrate
alpha 3 beta 1, Jnk, KLHL2, LGALS8, MSN, MT1G, MTF1,
Metabolism
NET1, phosphatase, PI4KA, PRDX4, PTGFRN, RNASEL,
ROR2, SACM1L, TMC6, TMC8, TNN, TSPAN, TSPAN4
Dankort D, Curley D P, Cartlidge R A Nelson B, Karnezis A N, Damsky W E Jr, You M J, DePinho R A, McMahon M, Rosenberg M. 2009. Braf V600E cooperates with Pten loss to induce metastatic melanoma. Nat Genet. 41(5):544-52.
Goel V K, Lazar A J F, Warneke C L, Redston M S, Haluska F G. 2006. Examination of Mutations in BRAF, NRAS, and PTEN in primary cutaneous melanoma. J Invest Dermatol. 126:154-160.
Schreck R, Rapp U R. 2006. Raf kinases: Oncogenesis and drug discovery. Int J Cancer. 119: 2261-2271.
Coopersmith, Robert, Lehrer, Raphael
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